{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# New Topic Tool AB Test Report: Logged-In User Analysis\n", "\n", "**Megan Neisler, Senior Data Scientist, Wikimedia Foundation**\n", "\n", "** 15 May 2022**\n", "\n", "[TASK](https://phabricator.wikimedia.org/T277825) | [CODEBASE](https://github.com/wikimedia-research/New-discussion-tool-analysis-2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Table of Contents\n", "\n", "1. [Introduction](#Introduction)\n", "2. [Methodology](#Methodology)\n", "3. [New Topic Tool Completion Rate](#New-Topic-Tool-Completion-Rate)\n", "4. [Guardrail Analyses](#Guardrail-Analyses)\n", "5. [Curiosities](#Curiosities)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Wikimedia Foundation's [Editing team](https://www.mediawiki.org/wiki/Editing_team#:~:text=The%20Editing%20team%20is%20the,tools%20like%20TemplateData%20and%20Citoid.) is working to improve how contributors communicate on Wikipedia using talk pages through a series of incremental improvements that will be released over time.\n", "\n", "As part of this effort, the Editing team introduced a new workflow for starting new topic threads on talk pages, across Wikipedia's 16 talk namespaces. This new workflow is intended to make it more intuitive for Junior Contributors to initiate conversations in ways other contributors can easily reply to and to help Senior Contributors do the same, with less effort. \n", "\n", "\n", "The new topic tool tool provides an an inline form for adding new topics. In addition, the language throughout the workflow was adjusted to be more topic-specific.\n", "\n", "The team ran an AB test of the New Topic Tool from 27 January 2022 through 25 March 2022 [^timeline] to assess the efficacy of this new feature. The test included all logged-in and logged-out users that edited a talk page at one the 20 participating Wikipedias during the duration of the AB test (see full list of [participating Wikipedias in task description](https://phabricator.wikimedia.org/T277825) and conditions outlined in the methodology section below). During this test, 50% of users included in the test had the New Topic tool automatically enabled, and 50% did not. This report focues on the results for logged-in users. \n", "\n", "[^timeline]: Note that we excluded logged-out data collected from from 27 January 2022 to 17 February 2022 in this analysis due to errors in the bucketing implementation that did not accurately log all the events in the AB test.\n", "\n", "You can find more information about features of this tool and project updates on the [project page](https://www.mediawiki.org/wiki/Talk_pages_project/New_discussion)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Methodology\n", "\n", "The AB test was run on a per Wikipedia basis and contributors included in the test were randomly assigned to either the control (new topic tool disabled by default) or treatment (new topic tool enabled by default). We excluded logged-in users that have previously used the New Topic tool (as determined by the state of the `discussiontools-newtopictool-opened` preference) but included users that had explicitly enabled or disabled the overall Discussion tools beta feature. Users at these Wikipedias were still able to turn the tool on or off the tool in Special:Preferences; however, they remained in the same group they were buketed in for the duration of the test. \n", "\n", "Upon conclusion of the test on 25 March 2022, we recorded a total of 19,676 new topic attempts initiated across both test groups by 6,258 distinct contributors across all experience levels. A total of 3,630 (58%) of these contributors were identified as Junior Contributors. Data was collected in [EditAttemptStep](https://gerrit.wikimedia.org/r/plugins/gitiles/schemas/event/secondary/+/refs/heads/master/jsonschema/analytics/legacy/editattemptstep/) and [talk_page_edit](https://schema.wikimedia.org/repositories//secondary/jsonschema/analytics/mediawiki/talk_page_edit/current.yaml) .\n", "\n", "In this test, a user can add a new topic using the New Topic Tool or using the existing add new section link interface. For the purpose of this analysis, these two types of editing experiences are defined as follows: \n", "\n", "**New Topic Tool:** Any edit to add a new topic (section) to a talk page namespace made with the new topic tool. The new topic tool allows edits using both wikitext and source mode. New Topic tool events were sampled at 100%. \n", "\n", "Recorded in EditAttemptStep as: `event.action = 'init'`, `event.integration = 'discussiontools'`, `event.init_type = 'section'`\n", "\n", "\n", "**Existing Add New Section Link:** Any edit to add a new topic (section) using the existing `section=new` link and workflow. These events were sampled at a rate of 1/16, or 6.125%. \n", "\n", "Recorded in the EditAttemptStep as: `event.action = 'init', event.integration = 'page' , event.init_type = 'section', event.init_mechanism IN ('url-new', 'new')` \n", "\n", "We excluded the following types of edits from this analysis: (1) edits made with the reply tool, (2) full-page edits to create a new page or to an existing page, (3) corrective edits to an existing section. Note: It's possible to create a new section using full page editing but these were excluded for the following reasons: (1) we do not have instrumentation to decipher beween an attempt to make an edit to existing text on the page vs an edit attempt to create a new section using full page editing; and (2) this method exists in both the test and control groups and we do not believe would be impacted by the appearance of the new topic tool. \n", "\n", "See the following Phabricator tickets for further details regarding instrumentation and implementation of the AB test:\n", "\n", "* Implement AB test bucketing [T291307](https://phabricator.wikimedia.org/T291307)\n", "* Start AB test [T291308](https://phabricator.wikimedia.org/T291308)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(IRdisplay)\n", "\n", "display_html(\n", "'\n", "
\n", " \n", "
'\n", ")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "shhh <- function(expr) suppressPackageStartupMessages(suppressWarnings(suppressMessages(expr)))\n", "shhh({\n", " library(tidyverse)\n", " # Modeling \n", " library(brms)\n", " library(lme4)\n", " library(tidybayes)\n", " set.seed(5)\n", " # Tables:\n", " library(gt)\n", " library(gtsummary)\n", "})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width = 15, repr.plot.height = 10)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# collect all new topic tool attempts and saves\n", "# Add a join with talk_page_edit to ensure all saved edits are included due to bug https://phabricator.wikimedia.org/T305541\n", "query <-\n", "\"\n", "--find all edit attempts\n", "WITH edit_attempts AS (\n", " SELECT\n", " wiki AS wiki,\n", " event.user_id as user_id,\n", " event.editing_session_id as edit_attempt_id,\n", " event.bucket AS experiment_group,\n", " event.is_oversample AS is_oversample,\n", " event.integration AS editing_method,\n", " If(event.integration == 'discussiontools', 1, 0) AS new_topic_tool_used,\n", " CASE\n", " WHEN event.init_type = 'section' AND event.integration == 'discussiontools' THEN 'new_topic_tool'\n", " WHEN event.init_type = 'section' AND event.integration == 'page' AND event.init_mechanism IN ('url-new', 'new') THEN 'new_section_link'\n", " ELSE 'NA' -- check to make sure all edit types accounted for in above list\n", " END AS section_edit_type,\n", " event.user_editcount AS experience_level\n", " FROM event.editattemptstep\n", " WHERE\n", "-- only in participating wikis\n", " wiki IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", "-- since deployment\n", " AND year = 2022\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " -- remove bots\n", " AND useragent.is_bot = false\n", "-- look at only desktop events\n", " AND event.platform = 'desktop'\n", "-- review all talk namespaces\n", " AND event.page_ns % 2 = 1\n", "-- only users in AB test\n", " AND event.bucket IN ('test', 'control')\n", "-- only registered user\n", " AND event.user_id != 0\n", " AND event.action = 'init'\n", "-- discard VE/Wikieditor edits to create new page or reply tool edits\n", " AND NOT (\n", " -- not a reply tool edit\n", " (event.init_type = 'page' AND event.integration = 'discussiontools') OR\n", " -- not an wikitext edit to create a new page\n", " (event.init_type = 'page' AND event.init_mechanism IN ('url-new', 'new') AND event.integration = 'page') OR\n", " -- not a corrective edit to an existing section\n", " (event.init_type = 'section' AND event.init_mechanism IN ('click', 'url') AND event.integration == 'page') OR\n", "-- not a full page edit\n", " (event.init_type = 'page' AND event.init_mechanism IN ('click', 'url') AND event.integration = 'page')\n", " )),\n", "\n", "-- find all published comments\n", "published_dt_new_topics AS (\n", " SELECT\n", " performer.user_id AS user_id,\n", " session_id AS edit_save_id,\n", " `database` AS wiki\n", " FROM event.mediawiki_talk_page_edit\n", " WHERE\n", " year = 2022\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " -- only in participating wikis\n", " AND `database` IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", "),\n", "\n", "published_section_link_new_topics AS (\n", "SELECT\n", " event.user_id as user_id,\n", " event.editing_session_id AS edit_save_id,\n", " wiki AS wiki\n", " FROM event.editattemptstep\n", " WHERE\n", " -- only in participating wikis\n", " wiki IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", " AND year = 2022\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " AND event.action = 'saveSuccess'\n", ")\n", "\n", "\n", "-- main query\n", "SELECT\n", " eas.wiki,\n", " eas.user_id,\n", " edit_attempt_id,\n", " experiment_group,\n", " is_oversample,\n", " editing_method,\n", " new_topic_tool_used,\n", " section_edit_type,\n", "-- was saved in either talk page edit or editattemptstep\n", " IF ((section_edit_type = 'new_topic_tool' AND (tpe_save.edit_save_id IS NOT NULL OR eas_save.edit_save_id IS NOT NULL))\n", " OR (section_edit_type = 'new_section_link' AND (tpe_save.edit_save_id IS NOT NULL OR eas_save.edit_save_id IS NOT NULL)), 1, 0) AS edit_success,\n", " experience_level\n", "FROM edit_attempts eas\n", "LEFT JOIN published_dt_new_topics tpe_save ON\n", " eas.edit_attempt_id = tpe_save.edit_save_id AND\n", " eas.wiki = tpe_save.wiki\n", "LEFT JOIN published_section_link_new_topics eas_save ON\n", " eas.edit_attempt_id = eas_save.edit_save_id AND\n", " eas.wiki = eas_save.wiki\n", "\"\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Don't forget to authenticate with Kerberos using kinit\n", "\n" ] } ], "source": [ "new_topic_attempts <- wmfdata::query_hive(query)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# data reformatting and cleanup\n", "\n", "#set factor levels with correct baselines\n", "new_topic_attempts$section_edit_type <-\n", " factor(\n", " new_topic_attempts$section_edit_type,\n", " levels = c(\"NA\", \"full_page_edits\", \"new_section_link\", \"new_topic_tool\"),\n", " labels = c(\"NA\", \"Full page edits\", \"Existing add new section link\", \"New topic tool\")\n", " )\n", "\n", "new_topic_attempts$edit_success <-\n", " factor(\n", " new_topic_attempts$edit_success,\n", " levels = c(0, 1),\n", " labels = c(\"Not Complete\", \"Complete\")\n", " )\n", "\n", "\n", "# reformat user-id and adjust to include wiki to account for duplicate user id instances.\n", "# Users do not have the smae user_id on different wikis\n", "new_topic_attempts$user_id <-\n", " as.character(paste(new_topic_attempts$user_id, new_topic_attempts$wiki, sep =\"-\"))\n", "\n", "#clarfiy wiki names\n", "new_topic_attempts <- new_topic_attempts %>%\n", " mutate(\n", " wiki = case_when(\n", " #clarfiy participating project names\n", " wiki == 'amwiki' ~ \"Amharic Wikipedia\",\n", " wiki == 'bnwiki' ~ \"Bengali Wikipedia\",\n", " wiki == 'zhwiki' ~ \"Chinese Wikipedia\",\n", " wiki == 'nlwiki' ~ 'Dutch Wikipedia',\n", " wiki == 'arzwiki' ~ 'Egyptian Wikipedia',\n", " wiki == 'frwiki' ~ 'French Wikipedia',\n", " wiki == 'hewiki' ~ 'Hebrew Wikipedia',\n", " wiki == 'hiwiki' ~ 'Hindi Wikipedia',\n", " wiki == 'idwiki' ~ 'Indonesian Wikipedia',\n", " wiki == 'itwiki' ~ 'Italian Wikipedia', \n", " wiki == 'jawiki' ~ 'Japanese Wikipedia', \n", " wiki == 'kowiki' ~ 'Korean Wikipedia',\n", " wiki == 'omwiki' ~ 'Oromo Wikipedia', \n", " wiki == 'fawiki' ~ 'Persian Wikipedia', \n", " wiki == 'plwiki' ~ 'Polish Wikipedia', \n", " wiki == 'ptwiki' ~ 'Portuguese Wikipedia',\n", " wiki == 'eswiki' ~ 'Spanish Wikipedia',\n", " wiki == 'thwiki' ~ 'Thai Wikipedia', \n", " wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',\n", " wiki == 'viwiki' ~ 'Vietnamese Wikipedia' \n", " )\n", " ) " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Create new column to identify Junior and Non-Junior Contributors\n", "\n", "new_topic_attempts <- new_topic_attempts %>%\n", " mutate(\n", " is_junior = case_when(\n", " #clarfiy participating project names\n", " experience_level < 100 ~ \"Junior Contributor\",\n", " experience_level >= 100 ~ \"Non-Junior Contributor\" \n", " ),\n", " is_junior = factor(is_junior,\n", " levels = c(\"Non-Junior Contributor\", \"Junior Contributor\")\n", " ))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New topic attempts in each group\n", "\n", "Since all Discussion Tool events including new topic tool events are oversampled (100% sampling rate) in EditAttemptStep, we removed oversampled events so we can directly compare the balance between each experiment group." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 3
experiment_groupusersattempts
<chr><int><int>
control8911392
test 8811549
\n" ], "text/latex": [ "A tibble: 2 × 3\n", "\\begin{tabular}{lll}\n", " experiment\\_group & users & attempts\\\\\n", " & & \\\\\n", "\\hline\n", "\t control & 891 & 1392\\\\\n", "\t test & 881 & 1549\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 3\n", "\n", "| experiment_group <chr> | users <int> | attempts <int> |\n", "|---|---|---|\n", "| control | 891 | 1392 |\n", "| test | 881 | 1549 |\n", "\n" ], "text/plain": [ " experiment_group users attempts\n", "1 control 891 1392 \n", "2 test 881 1549 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_attempts_bygroup <- new_topic_attempts %>%\n", " filter(is_oversample == 'false') %>% #All Discussion Tool events are oversampled - removing to check balance.\n", " group_by(experiment_group) %>%\n", " summarise(users = n_distinct(user_id),\n", " attempts = n_distinct(edit_attempt_id), .groups = 'drop')\n", "\n", "new_topic_attempts_bygroup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# New Topic Tool Completion Rate\n", "\n", "Our key performance indicator (KPI) for this analysis is new topic completion rate. For the purpose of this analysis, we are defining the completion rate as the percent of contributors that successfully published (`event.action = 'saveSuccess'` in EditAttemptStep)[^instrumentation] at least one comment after clicking the Add topic / New section link interface (`event.action = 'init'`) during the time of the AB test.\n", "\n", "Note that this does not take into account the number of attempts it took for the user to publish or the duration of their editing sessions. For comparison purposes, we also reviewed completion rate defined as the percent of all new topic edit attempts by Junior Contributors that were successfully published. This was also the dataset we used to model the impact of the new topic tool as the model accounts for both user and wiki experience on the success of each edit attempt.\n", "\n", "[^instrumentation]: During this analysis, we identified a [bug](https://phabricator.wikimedia.org/T305541) where some instances of edits sessions completed using the new topic tool were not correctly recorded as being sucessfully saved in EditAttemptStep. As a result, we also used the talk_page_edit schema to more accurately account for all edits posted by the new topic tool by joining this data with init events identifed in EditAttemptStep. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Topic Tool Completion Rate by Junior Contributors\n", "\n", "We first calculated the new topic completion rate for Junior Contributors overall and across each participating Wikipedia. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overall new topic completion rate by Junior Contributors " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### New topic completion rate defined as percent of edit attempts that are successfully published" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Completion Rate By Session\n", "\n", "new_topic_attempts_jc_bysession <- new_topic_attempts %>%\n", " filter(is_junior == 'Junior Contributor') %>% #only jc \n", " group_by (wiki, section_edit_type) %>%\n", " summarise(n_attempts = n_distinct(edit_attempt_id),\n", " n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),\n", " #n_completions = sum(edit_success == 'Complete'),\n", " new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),\n", " .groups = 'drop') \n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A tibble: 2 × 4
section_edit_typen_attemptsn_attempts_completedcompletion_rate
<fct><int><int><chr>
Existing add new section link 476 16534.7%
New topic tool 5799205435.4%
\n" ], "text/latex": [ "A tibble: 2 × 4\n", "\\begin{tabular}{llll}\n", " section\\_edit\\_type & n\\_attempts & n\\_attempts\\_completed & completion\\_rate\\\\\n", " & & & \\\\\n", "\\hline\n", "\t Existing add new section link & 476 & 165 & 34.7\\%\\\\\n", "\t New topic tool & 5799 & 2054 & 35.4\\%\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 2 × 4\n", "\n", "| section_edit_type <fct> | n_attempts <int> | n_attempts_completed <int> | completion_rate <chr> |\n", "|---|---|---|---|\n", "| Existing add new section link | 476 | 165 | 34.7% |\n", "| New topic tool | 5799 | 2054 | 35.4% |\n", "\n" ], "text/plain": [ " section_edit_type n_attempts n_attempts_completed completion_rate\n", "1 Existing add new section link 476 165 34.7% \n", "2 New topic tool 5799 2054 35.4% " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# By session\n", "\n", "new_topic_attempts_jc_bysession_all <- new_topic_attempts_jc_bysession %>%\n", " group_by(section_edit_type) %>%\n", " summarise(n_attempts = sum(n_attempts), \n", " n_attempts_completed = sum(n_completions), \n", " completion_rate = paste0(round(n_attempts_completed / n_attempts *100, 1), \"%\"),\n", " .groups = 'drop'\n", " ) \n", "new_topic_attempts_jc_bysession_all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### New topic completion rate defined as percent of Junior Contributors that successfully publish at least 1 new topic" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Completion Rate By Junior Contributors\n", "new_topic_attempts_jc <- new_topic_attempts %>%\n", " filter(is_junior == 'Junior Contributor') %>% #only jc \n", " group_by (wiki, section_edit_type, user_id) %>%\n", " summarise(n_attempts = n_distinct(edit_attempt_id),\n", " n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),\n", " edit_success = ifelse(sum(n_completions >= 1), 'Complete', 'Not Complete'), #redefine edit success as user completed at least 1 edit attempt\n", " new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),\n", " .groups = 'drop') \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# By Contributor\n", "# Review edit completion rate by editing method\n", "new_topic_attempts_jc_all <- new_topic_attempts_jc %>%\n", " group_by(section_edit_type) %>%\n", " summarise(n_users = n_distinct(user_id), \n", " n_users_completed = sum(n_completions >= 1), #user completed at least 1 edit\n", " completion_rate = paste0(round(n_users_completed / n_users *100, 1), \"%\"),\n", " .groups = 'drop'\n", " ) " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior contributors new topic completion rate
across all participating Wikipedias
Editing methodNumber of users attemptedNumber of users completedNew topic completion rate1
Existing add new section link243916036.4%
New topic tool33191141944.5%
\n", "

\n", " \n", " 1\n", " \n", " \n", " Defined as percent of contributors that attempted and published at least 1 new topic\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Sampling rate for Non-New Topic Tool events is 6.25%\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " Sampling rate for New Topic Tool events is 100%\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create table of completion rate\n", "new_topic_attempts_jc_all_table <- new_topic_attempts_jc_all %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior contributors new topic completion rate\",\n", " subtitle = \"across all participating Wikipedias\"\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing method\",\n", " n_users = \"Number of users attempted\",\n", " n_users_completed = \"Number of users completed\",\n", " completion_rate = \"New topic completion rate\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of contributors that attempted and published at least 1 new topic\",\n", " locations = cells_column_labels(\n", " columns = 'completion_rate'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for Non-New Topic Tool events is 6.25%\",\n", " locations = cells_body(\n", " columns = 'section_edit_type', rows = 1)\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for New Topic Tool events is 100%\",\n", " locations = cells_body(\n", " columns = 'section_edit_type', rows = 2)) %>%\n", " gtsave(\n", " \"new_topic_attempts_jc_all_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(data = new_topic_attempts_jc_all_table, file = \"new_topic_attempts_jc_all_table.html\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "\n", "p <- new_topic_attempts_jc_all %>%\n", " ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " scale_y_continuous(labels = scales::percent) +\n", " scale_x_discrete(labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " labs (y = \"Percent of junior contributors \",\n", " x = \"Editing method\",\n", " title = \"Junior contributors new topic completion rate \\n across all participating Wikipedias\",\n", " caption = \"Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position= \"none\",\n", " axis.line = element_line(colour = \"black\")) \n", " \n", "\n", "p\n", "ggsave(\"Figures/new_topic_attempts_jc_all.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Overall, 44.5% of all Junior Contributors that made a new topic attempt were able to successfully publish at least 1 new topic with the new topic tool, while 36.4% of all Junior Contributors successfully published a new topic using the previous new section link editing workflow. This represents a 8 percentage point (36.4% → 44.5%); 22% observed increase in new topic completion rate. \n", "\n", "Note that this does not take into account the number of attempts it took for the user to publish or the duration of their editing sessions. If we look just at the percent of editing sessions succesfully completed, there was a 0.7 percentage point (34.7% → 35.4%); 2% observed increase in new topic completion rate for editing sessions completed using the new topic tool. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### New topic completion rate by participating Wikipedia " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Review edit attempts by editing method and wiki\n", "new_topic_attempts_jc_bywiki <- new_topic_attempts_jc %>%\n", " group_by(wiki, section_edit_type) %>%\n", " summarise(n_users = n_distinct(user_id), \n", " n_users_completed = sum(n_completions >=1), #user completed at least 1 edit\n", " completion_rate = paste0(round(n_users_completed / n_users *100, 1), \"%\"),\n", " .groups = 'drop') %>%\n", " #determine credible intervals\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$n_users_completed, n = .$n_users, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))\n", " \n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior contributors new topic completion rate by participating Wikipedia
WikipediaEditing method1,2Number of users attemptedNumber of users completedCompletion rate3CI (Lower Bound)4CI (Upper Bound)4
Amharic WikipediaNew topic tool2150%0.060.94
Bengali WikipediaExisting add new section link6233.3%0.050.68
Bengali WikipediaNew topic tool501632%0.200.45
Chinese WikipediaExisting add new section link581017.2%0.090.28
Chinese WikipediaNew topic tool2536324.9%0.200.30
Dutch WikipediaExisting add new section link5240%0.080.77
Dutch WikipediaNew topic tool1035149.5%0.400.59
Egyptian WikipediaNew topic tool11763.6%0.360.88
French WikipediaExisting add new section link713143.7%0.320.55
French WikipediaNew topic tool55029854.2%0.500.58
Hebrew WikipediaExisting add new section link9666.7%0.370.91
Hebrew WikipediaNew topic tool1286853.1%0.450.62
Hindi WikipediaExisting add new section link9222.2%0.030.50
Hindi WikipediaNew topic tool401742.5%0.280.58
Indonesian WikipediaExisting add new section link281035.7%0.190.53
Indonesian WikipediaNew topic tool1224637.7%0.290.46
Italian WikipediaExisting add new section link501734%0.220.47
Italian WikipediaNew topic tool36518149.6%0.440.55
Japanese WikipediaExisting add new section link19736.8%0.170.58
Japanese WikipediaNew topic tool26411543.6%0.380.50
Korean WikipediaExisting add new section link10550%0.220.78
Korean WikipediaNew topic tool481633.3%0.210.47
Persian WikipediaExisting add new section link613049.2%0.370.62
Persian WikipediaNew topic tool2259240.9%0.350.47
Polish WikipediaExisting add new section link14750%0.260.74
Polish WikipediaNew topic tool1305643.1%0.350.52
Portuguese WikipediaExisting add new section link14321.4%0.050.44
Portuguese WikipediaNew topic tool1897841.3%0.340.48
Spanish WikipediaExisting add new section link531426.4%0.160.39
Spanish WikipediaNew topic tool49621944.2%0.400.49
Thai WikipediaExisting add new section link500%0.000.31
Thai WikipediaNew topic tool321134.4%0.190.51
Ukrainian WikipediaExisting add new section link100%0.000.77
Ukrainian WikipediaNew topic tool824453.7%0.430.64
Vietnamese WikipediaExisting add new section link261453.8%0.350.72
Vietnamese WikipediaNew topic tool1014039.6%0.300.49
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate for Non-New Topic Tool events is 6.25%\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Sampling rate for New Topic Tool events is 100%\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic\n", "
\n", "

\n", "

\n", " \n", " 4\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "NULL" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_attempts_jc_bywiki_tbl <- new_topic_attempts_jc_bywiki %>%\n", " select(c(1,2,3,4,5,12,13)) %>% #remove unneeded rows\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior contributors new topic completion rate by participating Wikipedia\"\n", " ) %>%\n", " cols_label(\n", " wiki = \"Wikipedia\",\n", " section_edit_type= \"Editing method\",\n", " n_users = \"Number of users attempted\",\n", " n_users_completed = \"Number of users completed\",\n", " completion_rate = \"Completion rate\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic\",\n", " locations = cells_column_labels(\n", " columns = 'completion_rate'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for Non-New Topic Tool events is 6.25%\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " ) \n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for New Topic Tool events is 100%\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )) %>%\n", " gtsave(\n", " \"new_topic_attempts_jc_bywiki_tbl .html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(data = new_topic_attempts_jc_bywiki_tbl, file = \"new_topic_attempts_jc_bywiki_tbl .html\")\n", "\n", "new_topic_attempts_jc_bywiki_tbl " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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w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\nnj9UEIR7772XiGpqajo7O1NSUh588EE1sC6Kotl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QUAAID20GtUnis1iciBs7Y/761Tlmnyktnu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\ndXV1T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Gh4vMzi9NNi63zrb7uumVlO4HX5LeqZ538GScXFib8sVl4/GS7s45jm622W86PXxTS7tK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\nNoVC0X+Bhd7mzZvnHBVtaWkxm806nU5yq6z+hAkTqqqqy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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Plot edit completion rates for each wiki \n", "\n", "dodge <- position_dodge(width=0.9)\n", "\n", "p <- new_topic_attempts_jc_bywiki %>%\n", " filter(!(wiki %in% c('Amharic Wikipedia', 'Egyptian Wikipedia', 'Thai Wikipedia'))) %>% # remove wikis where there are under 10 events as we \n", " ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +\n", " geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 5, color = \"white\") +\n", " facet_wrap(~ wiki) +\n", " scale_y_continuous(labels = scales::percent) +\n", " labs (y = \"Percent of junior contributors \",\n", " title = \"Junior contributors new topic completion rate by participating Wikipedia\",\n", " caption = \"Amharic, Egyptian, Thai, and Oromo Wikipedias removed from analysis due to insufficient events \\n\n", " Red error bars: Reflect 95% credible intervals\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\"), name = \"Editing Method\", labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "\n", " \n", "p\n", "ggsave(\"Figures/new_topic_attempts_jc_bywiki_plot.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trends vary on a per wikipedia basis. \n", "\n", "While the majority of participating Wikipedias saw a higher new topic completion rate with the new topic tool, 6 of the participating Wikipedias (Bengali, Hebrew, Korean, Persian, Polish, Vietnamese) saw a slightly higher new topic completion rates with the existing new section link editing workflow.\n", "\n", "It is important to note that there is a high level of uncertainty around many of these per Wikipedia completion rate values due to the smaller number of edit attempts recorded on a per Wikipedia basis, especially for smaller sized Wikipedias and for existing add new section link events which were sampled at only 6.25% (as represented by larger red error bars on the chart above). We can be more confident in the identified completion rate values for Wikipedias with more data and smaller error bars such as French and Spanish Wikipedia.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modeling the impact of the new topic tool" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We next explored different models to correctly infer the impact of the new topic tool on whether a new topic was completed or not and account for the random effects by the user and wiki. This allows us to confirm if the observed increase above is statistically significant (did not occur due to random chance).\n", "\n", "New topic attempts completed on the same Wikipedia and by the users on that Wikipedia are related to each other. Therefore, we can more accurately infer the impact of the new topic tool by accounting for the effect of the user and wiki on the success probability of a Junior Contributor completing an edit.\n", "\n", "We used a [Bayesian Hierarchical regression model](https://en.wikipedia.org/wiki/Bayesian_hierarchical_modeling) to model this structure. For this model, we reviewed whether each edit attempt was sucesfully completed or not. We identified the user and Wikipedia as random effects and whether the new topic tool was used as the fixed effect or predictor variable." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#redefine edit success as factor for use in the model\n", "new_topic_attempts_jc$edit_success <-\n", " factor(\n", " new_topic_attempts_jc$edit_success,\n", " levels = c(\"Not Complete\", \"Complete\")\n", " )" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "priors <- c(\n", " set_prior(prior = \"std_normal()\", class = \"b\"),\n", " set_prior(\"cauchy(0, 5)\", class = \"sd\")\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fit_jc <- brm(\n", " edit_success ~ section_edit_type + (1 | wiki/user_id),\n", " family = bernoulli(link = \"logit\"),\n", " data = new_topic_attempts_jc,\n", " prior = priors,\n", " chains = 4, cores = 4\n", ")" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "fit_jc_tbl <- fit_jc %>%\n", " spread_draws(b_section_edit_typeNewtopictool, b_Intercept) %>%\n", " mutate(\n", " exp_b = exp(b_section_edit_typeNewtopictool),\n", " b4 = b_section_edit_typeNewtopictool/ 4,\n", " avg_lift = plogis(b_Intercept + b_section_edit_typeNewtopictool) - plogis(b_Intercept)\n", " ) %>%\n", " pivot_longer(\n", " b_section_edit_typeNewtopictool:avg_lift,\n", " names_to = \"param\",\n", " values_to = \"val\"\n", " ) %>%\n", " group_by(param) %>%\n", " summarize(\n", " ps = c(0.025, 0.5, 0.975),\n", " qs = quantile(val, probs = ps),\n", " .groups = \"drop\"\n", " ) %>%\n", " mutate(\n", " quantity = ifelse(\n", " param %in% c(\"b_Intercept\", \"b_section_edit_typeNewtopictool\"),\n", " \"Parameter\", \"Function of parameter(s)\"\n", " ),\n", " param = factor(\n", " param,\n", " c(\"b_Intercept\", \"b_section_edit_typeNewtopictool\", \"exp_b\", \"b4\", \"avg_lift\"),\n", " c(\"(Intercept)\", \"Using new topic tool\", \"Multiplicative effect on odds\", \"Maximum Lift\", \"Average lift\")\n", " ),\n", " ps = factor(ps, c(0.025, 0.5, 0.975), c(\"lower\", \"median\", \"upper\")),\n", " ) %>%\n", " pivot_wider(names_from = \"ps\", values_from = \"qs\") %>%\n", " arrange(quantity, param)\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior Contributor Completion Rate: Posterior summary of model parameters
Point Estimate95% CI1
Parameter
(Intercept)−0.606(−1.032, −0.319)
Using new topic tool0.277(0.054, 0.533)
Function of parameter(s)
Multiplicative effect on odds1.319(1.055, 1.704)
Maximum Lift6.9%2(1.3%, 13.3%)
Average lift6.5%3(1.3%, 12.0%)
\n", "

\n", " \n", " 1\n", " \n", " \n", " CI: Credible Interval\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Maximum lift calculated using the divide-by-4-rule\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " Average lift = Pr(Success|New Topic Tool) - Pr(Success|New Section Link Editing) = logit-10 + β1) - logit-10)\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fit_jc_tbl%>%\n", " gt(rowname_col = \"param\", groupname_col = \"quantity\") %>%\n", " row_group_order(c(\"Parameter\", \"Function of parameter(s)\")) %>%\n", " fmt_number(vars(lower, median, upper), decimals = 3) %>%\n", " fmt_percent(columns = vars(median, lower, upper), rows = 2:3, decimals = 1) %>%\n", " cols_align(\"center\", vars(median, lower, upper)) %>%\n", " cols_merge(vars(lower, upper), pattern = \"({1}, {2})\") %>%\n", " cols_move_to_end(vars(lower)) %>%\n", " cols_label(median = \"Point Estimate\", lower = \"95% CI\") %>%\n", " tab_style(cell_text(weight = \"bold\"), cells_row_groups()) %>%\n", " tab_footnote(\"CI: Credible Interval\", cells_column_labels(vars(lower))) %>%\n", " tab_footnote(\n", " html(\"Average lift = Pr(Success|New Topic Tool) - Pr(Success|New Section Link Editing) = logit-10 + β1) - logit-10)\"),\n", " cells_body(vars(median), 3)\n", " ) %>%\n", " tab_footnote(\n", " html(\"Maximum lift calculated using the divide-by-4-rule\"),\n", " cells_body(vars(median), 2)\n", " ) %>%\n", " tab_header(\"Junior Contributor Completion Rate: Posterior summary of model parameters\") %>%\n", " gtsave(\n", " \"fit_jc_tbl.html\", inline_css = TRUE)\n", "\n", "\n", "IRdisplay::display_html(file = \"fit_jc_tbl.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the model parameters are on the log-odds scale, we needed to apply the following transformations to make sense of them. \n", "* We used the \"divide-by-4\" rule suggested by Gelman, Hill, and Vehtari 2021 [^Gelman] to approximate the maximum increase in the probability of success corresponding to which editing interface (new topic tool or previous new section link workflow) was used. Using the bayesian model, we can also directly calculate the average lift.\n", "* Since the model parameters are on the log-odds scale, we need to take the exponentiation of the effect (exp(β~1~)) to determine the multiplicative effect on the odds of a Junior Contributor successfully publishing at least 1 new topic.\n", "\n", "[^Gelman]: Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2021. Regression and other stories. https://doi.org/10.1017/9781139161879.\n", "\n", "Based on estimates from the model, we found that Junior Contributors who open the new topic tool are about 1.3 times more likely to successfully publish a new topic than Junior Contributors who open the previous add new section link workflow. \n", "\n", "We also found there is an average 6.5% increase (maximum 6.9% increase) in the probability of a Junior Contributor publishing a new topic when they switch from using the previous add new section link workflow to the new topic tool. We can confirm statistical significance at the 0.05 level for all of these estimates (as indicated by credible intervals that do not cross 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accounting for Experience Level\n", "### Comment completion rate across all experience levels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the purpose of this AB test, we primarily focused on determining if the new topic tool had an impact on Junior Contributors’ new topic completion rate; however, we also reviewed the new topic tool impact across all contributors’ completion rates to provide insight into differences due to experience level.\n", "\n", "In this analysis, we've defined Junior Contributors as contributors with under 100 cumulative edits. To further investigate the impact of experience level, we also reviewed the new topic completion rate for non-junior experience levels (over 100 edits), breaking down experience level into edit segments (buckets).\n", "\n", "Note that this segmentation doesn't fully capture the gradual growth of an editor. For example, using these categories, a contributor with 99 edits would just have to make a 1 more edit to be redefined as a non-junior contributor and their probability of completing an edit would suddenly increase. Also, changes in lower edit counts (i.e. 1 to 2 edits) indicate a higher impact than changes in higher edit counts (e.g. 5,000 to 5,001 edits). However, we used the segementation as it aligns with how we've defined the target audience (Junior Contributors) and helps simplify the model for the purposes of this analysis.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first looked at the distribution of users in the test by edit count to help determine the experience level segments to use in the analysis." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Create histogram\n", "\n", "exp_histogram <- new_topic_attempts %>%\n", " filter(experience_level < 5000) %>% #remove significant outliers to help more clearly see distribution of majority\n", " group_by(user_id) %>%\n", " summarize(experience_level = min(experience_level), .groups = 'drop') %>% #experience level at first recorded attempt in AB test\n", " ggplot(aes(x=experience_level)) + \n", " geom_histogram(binwidth = 100, fill = 'steelblue2') +\n", " scale_x_continuous(labels = scales::comma, breaks=seq(0,5000,500)) +\n", " labs (title = \"Distribution of AB Test Users Edit Count \",\n", " y = \"Number of users\") + \n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "exp_histogram " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The distribution of AB test users' experience is highly left-skewed with the vast majority of users having under 500 edits with significant outliers. After 3000 edits, the number of users is minimal. We'll divide users into segments of 500[^segment size] to take a more granular look at the impact of experience level with an over 3000 group to account for the remaining outliers. \n", "\n", "[^segment size]:There is also a sample size issue we need to take into account. If we make the segments too small (or granular), there will not be enough events logged within that group to have a representative sample. This is expecially true for higher edit count groups where there are fewer users. We will need to make the segments large enough to include a sufficient sample size for review. \n" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "# divide experiene level groups\n", "new_topic_attempts_exp <- new_topic_attempts %>%\n", " mutate(experience_group = cut(as.numeric(experience_level), \n", " breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,\n", " Inf), \n", " labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',\n", " 'over 3000 edits'), include.lowest = TRUE))\n", "\n" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "#aggregate data to show contributors that completed at least 1 edit by experience level\n", "new_topic_attempts_byexp <- new_topic_attempts_exp %>%\n", " group_by (wiki, section_edit_type, user_id, experience_group) %>%\n", " summarise(n_attempts = n_distinct(edit_attempt_id),\n", " n_completions = sum(edit_success == 'Complete'),\n", " edit_success = ifelse(sum(n_completions >= 1), 'Complete', 'Not Complete'), #redefine edit success as user completed at least 1 edit\n", " new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),\n", " .groups = 'drop') \n" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ " #convert edit success as factor for use in the model\n", "new_topic_attempts_byexp$edit_success <- factor(\n", " new_topic_attempts_byexp$edit_success,\n", " levels = c(\"Not Complete\", \"Complete\"),\n", " labels = c(\"Not Complete\", \"Complete\")\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll review the completion rate across all experience levels." ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "# Review edit completion rate by section edit type across all experience levels\n", "new_topic_completes_allexp <- new_topic_attempts_byexp %>%\n", " group_by(section_edit_type) %>%\n", " summarise(n_users = n_distinct(user_id), \n", " n_users_completed = sum(n_completions >= 1), #user completed at least 1 edit\n", " completion_rate = paste0(round(n_users_completed / n_users *100, 1), \"%\"),\n", " .groups = 'drop'\n", " ) " ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Contributors new topic completion rate
Across all experience levels and participating Wikipedias
Editing methodNumber of users attemptedNumber of users completedCompletion rate1
Existing add new section link2113065357.8%
New topic tool35640313555.6%
\n", "

\n", " \n", " 1\n", " \n", " \n", " Defined as percent of contributors that make a new topic attempt and publish at least 1 comment.\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Sampling rate for Non-New Topic Tool events is 6.25%\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " Sampling rate for New Topic Tool events is 100%\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_completes_allexp_table <- new_topic_completes_allexp %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Contributors new topic completion rate\",\n", " subtitle = \"Across all experience levels and participating Wikipedias\"\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing method\",\n", " n_users = \"Number of users attempted\",\n", " n_users_completed = \"Number of users completed\",\n", " completion_rate = \"Completion rate\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of contributors that make a new topic attempt and publish at least 1 comment.\",\n", " locations = cells_column_labels(\n", " columns = 'completion_rate'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for Non-New Topic Tool events is 6.25%\",\n", " locations = cells_body(\n", " columns = 'section_edit_type', rows = 1)\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate for New Topic Tool events is 100%\",\n", " locations = cells_body(\n", " columns = 'section_edit_type', rows = 2)\n", " ) %>%\n", " gtsave(\n", " \"new_topic_completes_allexp_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_completes_allexp_table.html\")" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Plot edit completion rates across all wikis and experience levels \n", "\n", "p <- new_topic_completes_allexp %>%\n", " ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " scale_y_continuous(labels = scales::percent) +\n", " scale_x_discrete(labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " labs (y = \"Percent of contributors \",\n", " x = \"Editing method\",\n", " title = \"Contributors comment completion rate \\n across all experience levels and participating Wikipedias\",\n", " caption = \"Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position= \"none\",\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "\n", "p\n", "ggsave(\"Figures/new_topic_completes_allexp.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Across all contributor experience levels, there was a slight decrease (-2.2 percentage points (57.8% → 55.6%; 3.8% decrease) in the percent of contributors that were able to successfully publish a comment using the new topic tool compared to contributors using the existing add new section link workflow. This is different than the observed increase in Junior Contributors' new topic completion rates indicating that the experience level is a factor on the impact of the new topic tool. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comment completion rate by experience level\n", "\n", "Next, we'll look at completion rate by each of the identifed experience levels (i.e. cumulative edit) bins.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### New topic completion rate defined as percent of edit attempts that are successfully published" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 16 × 6
experience_groupsection_edit_typen_attemptsn_completionscompletion_ratenew_topic_tool_used
<fct><fct><int><int><chr><int>
0-100 edits Existing add new section link 512 17935% 0
0-100 edits New topic tool 6266224935.9%1
101-500 edits Existing add new section link 100 5050% 0
101-500 edits New topic tool 1813 96153% 1
501-1000 edits Existing add new section link 67 3755.2%0
501-1000 edits New topic tool 879 48455.1%1
1001-1500 editsExisting add new section link 26 1246.2%0
1001-1500 editsNew topic tool 509 29858.5%1
1501-2000 editsExisting add new section link 20 1680% 0
1501-2000 editsNew topic tool 510 34968.4%1
2001-2500 editsExisting add new section link 41 2970.7%0
2001-2500 editsNew topic tool 401 26265.3%1
2501-3000 editsExisting add new section link 16 1062.5%0
2501-3000 editsNew topic tool 267 16160.3%1
over 3000 editsExisting add new section link1183 96581.6%0
over 3000 editsNew topic tool 8667502257.9%1
\n" ], "text/latex": [ "A tibble: 16 × 6\n", "\\begin{tabular}{llllll}\n", " experience\\_group & section\\_edit\\_type & n\\_attempts & n\\_completions & completion\\_rate & new\\_topic\\_tool\\_used\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t 0-100 edits & Existing add new section link & 512 & 179 & 35\\% & 0\\\\\n", "\t 0-100 edits & New topic tool & 6266 & 2249 & 35.9\\% & 1\\\\\n", "\t 101-500 edits & Existing add new section link & 100 & 50 & 50\\% & 0\\\\\n", "\t 101-500 edits & New topic tool & 1813 & 961 & 53\\% & 1\\\\\n", "\t 501-1000 edits & Existing add new section link & 67 & 37 & 55.2\\% & 0\\\\\n", "\t 501-1000 edits & New topic tool & 879 & 484 & 55.1\\% & 1\\\\\n", "\t 1001-1500 edits & Existing add new section link & 26 & 12 & 46.2\\% & 0\\\\\n", "\t 1001-1500 edits & New topic tool & 509 & 298 & 58.5\\% & 1\\\\\n", "\t 1501-2000 edits & Existing add new section link & 20 & 16 & 80\\% & 0\\\\\n", "\t 1501-2000 edits & New topic tool & 510 & 349 & 68.4\\% & 1\\\\\n", "\t 2001-2500 edits & Existing add new section link & 41 & 29 & 70.7\\% & 0\\\\\n", "\t 2001-2500 edits & New topic tool & 401 & 262 & 65.3\\% & 1\\\\\n", "\t 2501-3000 edits & Existing add new section link & 16 & 10 & 62.5\\% & 0\\\\\n", "\t 2501-3000 edits & New topic tool & 267 & 161 & 60.3\\% & 1\\\\\n", "\t over 3000 edits & Existing add new section link & 1183 & 965 & 81.6\\% & 0\\\\\n", "\t over 3000 edits & New topic tool & 8667 & 5022 & 57.9\\% & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 16 × 6\n", "\n", "| experience_group <fct> | section_edit_type <fct> | n_attempts <int> | n_completions <int> | completion_rate <chr> | new_topic_tool_used <int> |\n", "|---|---|---|---|---|---|\n", "| 0-100 edits | Existing add new section link | 512 | 179 | 35% | 0 |\n", "| 0-100 edits | New topic tool | 6266 | 2249 | 35.9% | 1 |\n", "| 101-500 edits | Existing add new section link | 100 | 50 | 50% | 0 |\n", "| 101-500 edits | New topic tool | 1813 | 961 | 53% | 1 |\n", "| 501-1000 edits | Existing add new section link | 67 | 37 | 55.2% | 0 |\n", "| 501-1000 edits | New topic tool | 879 | 484 | 55.1% | 1 |\n", "| 1001-1500 edits | Existing add new section link | 26 | 12 | 46.2% | 0 |\n", "| 1001-1500 edits | New topic tool | 509 | 298 | 58.5% | 1 |\n", "| 1501-2000 edits | Existing add new section link | 20 | 16 | 80% | 0 |\n", "| 1501-2000 edits | New topic tool | 510 | 349 | 68.4% | 1 |\n", "| 2001-2500 edits | Existing add new section link | 41 | 29 | 70.7% | 0 |\n", "| 2001-2500 edits | New topic tool | 401 | 262 | 65.3% | 1 |\n", "| 2501-3000 edits | Existing add new section link | 16 | 10 | 62.5% | 0 |\n", "| 2501-3000 edits | New topic tool | 267 | 161 | 60.3% | 1 |\n", "| over 3000 edits | Existing add new section link | 1183 | 965 | 81.6% | 0 |\n", "| over 3000 edits | New topic tool | 8667 | 5022 | 57.9% | 1 |\n", "\n" ], "text/plain": [ " experience_group section_edit_type n_attempts n_completions\n", "1 0-100 edits Existing add new section link 512 179 \n", "2 0-100 edits New topic tool 6266 2249 \n", "3 101-500 edits Existing add new section link 100 50 \n", "4 101-500 edits New topic tool 1813 961 \n", "5 501-1000 edits Existing add new section link 67 37 \n", "6 501-1000 edits New topic tool 879 484 \n", "7 1001-1500 edits Existing add new section link 26 12 \n", "8 1001-1500 edits New topic tool 509 298 \n", "9 1501-2000 edits Existing add new section link 20 16 \n", "10 1501-2000 edits New topic tool 510 349 \n", "11 2001-2500 edits Existing add new section link 41 29 \n", "12 2001-2500 edits New topic tool 401 262 \n", "13 2501-3000 edits Existing add new section link 16 10 \n", "14 2501-3000 edits New topic tool 267 161 \n", "15 over 3000 edits Existing add new section link 1183 965 \n", "16 over 3000 edits New topic tool 8667 5022 \n", " completion_rate new_topic_tool_used\n", "1 35% 0 \n", "2 35.9% 1 \n", "3 50% 0 \n", "4 53% 1 \n", "5 55.2% 0 \n", "6 55.1% 1 \n", "7 46.2% 0 \n", "8 58.5% 1 \n", "9 80% 0 \n", "10 68.4% 1 \n", "11 70.7% 0 \n", "12 65.3% 1 \n", "13 62.5% 0 \n", "14 60.3% 1 \n", "15 81.6% 0 \n", "16 57.9% 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Completion Rate By Session\n", "\n", "new_topic_attempts_byexp_bysession <- new_topic_attempts_exp %>%\n", " group_by (experience_group, section_edit_type) %>%\n", " summarise(n_attempts = n_distinct(edit_attempt_id),\n", " n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),\n", " completion_rate = paste0(round(n_completions / n_attempts *100, 1), \"%\"),\n", " new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),\n", " .groups = 'drop') \n", "\n", "new_topic_attempts_byexp_bysession" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### New topic completion rate defined as percent of contributors that complete at least 1 new topic " ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "# Review edit completion rate by contributors and experience level\n", "new_topic_completes_byexp <- new_topic_attempts_byexp %>%\n", " group_by(experience_group, section_edit_type) %>%\n", " summarise(n_users = n_distinct(user_id), \n", " n_users_completed = n_distinct(user_id[n_completions >= 1]), #user completed at least 1 edit\n", " completion_rate = paste0(round(n_users_completed / n_users *100, 1), \"%\"),\n", " .groups = 'drop'\n", " ) %>% #determine credible intervals\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$n_users_completed, n = .$n_users, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))\n" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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Contributors new topic completion rate by experience level
Across all participating Wikipedias
Experience level groupEditing experienceNumber of users attemptedNumber of users completedCompletion rate1CI (Lower Bound)2CI (Upper Bound)2
0-100 editsExisting add new section link47317436.8%0.320.41
0-100 editsNew topic tool3507156244.5%0.430.46
101-500 editsExisting add new section link864552.3%0.420.63
101-500 editsNew topic tool61940865.9%0.620.70
501-1000 editsExisting add new section link472553.2%0.390.67
501-1000 editsNew topic tool22914362.4%0.560.69
1001-1500 editsExisting add new section link22940.9%0.220.61
1001-1500 editsNew topic tool1449163.2%0.550.71
1501-2000 editsExisting add new section link13969.2%0.440.90
1501-2000 editsNew topic tool1188269.5%0.610.77
2001-2500 editsExisting add new section link221463.6%0.440.82
2001-2500 editsNew topic tool996666.7%0.570.76
2501-3000 editsExisting add new section link10660%0.320.86
2501-3000 editsNew topic tool694058%0.460.69
over 3000 editsExisting add new section link47937778.7%0.750.82
over 3000 editsNew topic tool113780070.4%0.680.73
\n", "

\n", " \n", " 1\n", " \n", " \n", " Defined as percent of contributors that made a new topic attempt and publish at least 1 comment.\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "NULL" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_completes_byexp_table <- new_topic_completes_byexp %>%\n", " select(c(1,2,3,4,5,12,13)) %>% #remove unneeded rows\n", " gt() %>%\n", " tab_header(\n", " title = \"Contributors new topic completion rate by experience level\",\n", " subtitle = \"Across all participating Wikipedias\"\n", " ) %>%\n", " cols_label(\n", " experience_group = \"Experience level group\",\n", " section_edit_type = \"Editing experience\",\n", " n_users = \"Number of users attempted\",\n", " n_users_completed = \"Number of users completed\",\n", " completion_rate = \"Completion rate\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of contributors that made a new topic attempt and publish at least 1 comment.\",\n", " locations = cells_column_labels(\n", " columns = 'completion_rate'\n", " )) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )) %>%\n", " gtsave(\n", " \"new_topic_completes_byexp_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_completes_byexp_table.html\")\n", "new_topic_completes_byexp_table " ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "image/png": 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gT5yOQy2U2Nob7VWFSru1il+6ZE22ormRVC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\nhJDRWd/5cbdPK/2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6qqKsNnz8/PN7xJMKtDAgAA4LbFVHoAAHBn0lf99LRabUZGxk0vNN3myAzT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\nfdHqtxvSg5lBdh/64or/1PIEo2r7qhi/xk+qbO+V3u2m83ds6QRd3o1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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Plot edit completion rates for each user on each wiki \n", "\n", "p <- new_topic_completes_byexp %>%\n", " ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +\n", " facet_wrap(~ experience_group) +\n", " scale_y_continuous(labels = scales::percent) +\n", " labs (y = \"Percent of contributors \",\n", " title = \"Contributors new topic completion rate by experience level \\n across all participating Wikipedias\",\n", " caption = \"Red error bars: 95% credible intervals\"\n", " )+\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\"), name = \"Editing Method\", labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "p \n", "ggsave(\"Figures/new_topic_completes_byexp.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observations:\n", "* By breaking down the experience levels into more granular segments, we are able to isolate the decrease in completion rate to contributors with over 2500 edits [^errorbar].\n", " * There is higher new topic tool completion rate for all experience groups under 2500 cumulative edits, with percent increases ranging from only 0.4% for the 1501-2000 edit group level to a 54.5% increase for the 1001-1500 edit count group.\n", " * We observed a lower new topic tool completion rate for users with over 2500 cumulative edits (-3.4% decrease for the 2501-3000 edit count group and -10.7% decrease for the over 3000 edit count group).\n", "* When looking at topic completion rate by session (defined as percent of edit attempts that are successfully published), there is an even higher decrease in new topic tool completion rate for users with over 3000 edits (-23.8 percentage points (81.5% → 57.7%; -29% decrease). This potentially indicates that there a a few power users that are making a high number of incomplete New Topic Tool edits. \n", "\n", "[^errorbar]: The red error bars indicate the level of uncertainty in the identifed completion rates. There is more uncertainty in the identified completion rates for higher edit count contributors using the existing add new section link. This is due to the smaller sample size available for in these groups (There are fewer users in the higher level edit counts and these events are sampled at 6.25%). However, we can be more confident in the completion rate values for edit count groups with more data and smaller error bars such as the over 3000 edit count group. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling the impact of the new topic tool " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since new topic completion rates seem to vary based on the Contributor's experience level, we adjusted the Bayesian Hierarchical Regression Model to include the Contributors' experience level (junior or non-junior) as an interaction term in the model in addition to the effects of the user and wiki on comment completion rate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "fit_all <- brm(\n", " edit_success ~ section_edit_type * is_junior + (1 | wiki/user_id), \n", " family = bernoulli(link = \"logit\"),\n", " data = new_topic_attempts_byexp, \n", " prior = priors,\n", " chains = 4, cores = 4\n", ")" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "conditional_effects <- conditional_effects(fit_all, effects = \"is_junior:section_edit_type\")\n", "p <- plot(conditional_effects, plot = FALSE)[[1]] +\n", " labs (y = \"Probability of new topic completion\",\n", " title = \"Effects of experience level and editor type \\n on new topic completion probability\") +\n", " theme_bw() +\n", " scale_color_manual(values= c(\"#999999\", \"steelblue2\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.line = element_line(colour = \"black\")) \n", "p\n", "ggsave(\"Figures/conditional_effects_exp_editor.png\", p, width = 16, height = 8, units = \"in\", dpi = 300) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above plot shows the predicted effects of the Contributor's experience level (Junior or Non-Junior) and the type of editor that the used (new topic tool and existing add section link) on the probability of successfully publishing a comment that they started on a talk page. \n", "\n", "Based on the model, we can confirm the following: \n", "- Using either section editing method, there is a significant difference beween the probability of a Junior Contributor completing a new topic and a Non-Junior Contributor.\n", "- A Junior Contributor using the new topic tool is more likely to complete at least one new topic than when using the existing add new section link; however, they are still much less likely to complete a new topic using either method than a Non-Junior Contributor.\n", "- Non-Junior Contibutors using the new topic tool are less likely to complete at least one new topic than when using the existing add new section link. Based on a review of completion rates identifed for more granular segments of Non-Junior Contributors (documented in the previous section), we see this decrease primarily with contributors with over 2500 edits. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Guardrail Analyses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Topic Revert Rate " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also wanted to ensure that enabling the new topic tool did not result in an increase in the number of disruptive edits being made to talk pages. \n", "\n", "To evaluate any disruption caused by the new topic tool, we determined the percent of new topic published to talk pages that were reverted within 48 hours and the percent of contributors blocked after making a comment to a talk page. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methodology\n", "\n", "For this analysis, we reviewed data recorded in [mediawiki_history](https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake/Edits/MediaWiki_history) to identify the percent comments posted with the new topic tool (identified by the revision tag: `discussiontools-newtopic`) on talk pages that are reverted within 48 hours [^revert]. We joined this data with AB test data logged in editattemptstep and talk_page_edit to isolate data to the attempts included in the AB test and try to exclude any non new topic edits.\n", "\n", "[^revert]: 48 hours is a common cutoff, as research suggests that, at least for the English Wikipedia, nearly all reverts take place within 48 hours. Source: Research: Revert. Mediawiki. https://meta.wikimedia.org/wiki/Research:Revert.\n", "\n", "We compared the revert rate for new topics published using the new topic tool to the revert rate for new topics made using the existing add new section link worflow during the same timeframe. \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "new_topic_reverts <-\n", " read.csv(\n", " file = 'Data/new_topic_reverts.csv',\n", " header = TRUE,\n", " sep = \",\",\n", " stringsAsFactors = FALSE\n", " ) # loads all revert data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#clarfiy levels and lables for factor variables\n", "new_topic_reverts$section_edit_type <-\n", " factor(\n", " new_topic_reverts$section_edit_type,\n", " levels = c(\"non-new-topic-tool\", \"new-topic-tool\"),\n", " labels = c(\"Existing add new section link\", \"New topic tool\")\n", " )\n", "\n", "new_topic_reverts$is_reverted <-\n", " factor(new_topic_reverts$is_reverted,\n", " levels = c(\"reverted\", \"not-reverted\"),\n", " labels = c(\"Reverted\", \"Not reverted\"))\n", "\n", "#reformat user-id and adjust to include wiki to account for duplicate user id instances.\n", "# Users do not have the smae user_id on different wikis\n", "new_topic_reverts$user_id <-\n", " as.character(paste(new_topic_reverts$user_id,new_topic_reverts$wiki,sep =\"-\" ))\n", "\n", "#clarfiy wiki names\n", "new_topic_reverts <- new_topic_reverts %>%\n", " mutate(\n", " wiki = case_when(\n", " #clarfiy participating project names\n", " wiki == 'amwiki' ~ \"Amharic Wikipedia\",\n", " wiki == 'bnwiki' ~ \"Bengali Wikipedia\",\n", " wiki == 'zhwiki' ~ \"Chinese Wikipedia\",\n", " wiki == 'nlwiki' ~ 'Dutch Wikipedia',\n", " wiki == 'arzwiki' ~ 'Egyptian Wikipedia',\n", " wiki == 'frwiki' ~ 'French Wikipedia',\n", " wiki == 'hewiki' ~ 'Hebrew Wikipedia',\n", " wiki == 'hiwiki' ~ 'Hindi Wikipedia',\n", " wiki == 'idwiki' ~ 'Indonesian Wikipedia',\n", " wiki == 'itwiki' ~ 'Italian Wikipedia', \n", " wiki == 'jawiki' ~ 'Japanese Wikipedia', \n", " wiki == 'kowiki' ~ 'Korean Wikipedia',\n", " wiki == 'omwiki' ~ 'Oromo Wikipedia', \n", " wiki == 'fawiki' ~ 'Persian Wikipedia', \n", " wiki == 'plwiki' ~ 'Polish Wikipedia', \n", " wiki == 'ptwiki' ~ 'Portuguese Wikipedia',\n", " wiki == 'eswiki' ~ 'Spanish Wikipedia',\n", " wiki == 'thwiki' ~ 'Thai Wikipedia', \n", " wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',\n", " wiki == 'viwiki' ~ 'Vietnamese Wikipedia', \n", " )\n", " ) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Create new column to identify Junior and Non-Junior Contributors\n", "\n", "new_topic_reverts <- new_topic_reverts %>%\n", " mutate(\n", " is_junior = case_when(\n", " #clarfiy participating project names\n", " experience_level < 100 ~ \"Junior Contributor\",\n", " experience_level >= 100 ~ \"Non-Junior Contributor\" \n", " ),\n", " is_junior = factor(is_junior,\n", " levels = c(\"Non-Junior Contributor\", \"Junior Contributor\")\n", " ))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Junior Contributor revert rate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Overall revert rate across all participating Wikipedias" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# filter date to only look at junior contributor edits reverted\n", "new_topic_reverts_jc <- new_topic_reverts %>%\n", " filter(is_junior == 'Junior Contributor') " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# aggregrate based on editing experience type\n", "new_topic_reverts_jc_all <- new_topic_reverts_jc %>%\n", " group_by(section_edit_type) %>%\n", " summarise(total_reverts = n_distinct(revision_id[is_reverted == \"Reverted\"]),\n", " total_comments = n_distinct(revision_id),\n", " revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop') %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior contributors new topic revert rate across all participating Wikipedias
Across all participating Wikipedias
Editing experience1Number of new topics revertedNumber of new topics publishedRevert rate2CI (Lower Bound)3CI (Upper Bound)3
Existing add new section link2017411.49 %0.070.17
New topic tool20721659.56 %0.080.11
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Defined as percent of new topics reverted within 48 hours.\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_reverts_jc_all_table <- new_topic_reverts_jc_all %>%\n", " select(c(1,2,3,4,11,12)) %>% #remove unneeded rows\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior contributors new topic revert rate across all participating Wikipedias\",\n", " subtitle = \"Across all participating Wikipedias\"\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing experience\",\n", " total_reverts = \"Number of new topics reverted\",\n", " total_comments = \"Number of new topics published\",\n", " revert_rate = \"Revert rate\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of new topics reverted within 48 hours.\",\n", " locations = cells_column_labels(\n", " columns = 'revert_rate'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_reverts_jc_all_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_reverts_jc_all_table.html\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Plot edit completion rates for each user on each wiki \n", "\n", "dodge <- position_dodge(width=0.9)\n", "\n", "p <- new_topic_reverts_jc_all %>%\n", " ggplot(aes(x= section_edit_type, y = total_reverts/ total_comments, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +\n", " geom_text(aes(label = paste(revert_rate), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " scale_y_continuous(labels = scales::percent) +\n", " scale_x_discrete(labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " labs (y = \"Percent of new topics reverted \",\n", " x = \"Editing Method\",\n", " title = \"Junior contributors new topic revert rate across \\n all participating Wikipedias\",\n", " caption = \"Revert rate defined as percent of published new topics reverted within 48 hours \\n\n", " Red error bars: 95% credible intervals\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position= \"none\",\n", " axis.line = element_line(colour = \"black\"))\n", "p\n", "\n", "\n", "ggsave(\"Figures/new_topic_reverts_jc_all .png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Overall, across all participating Wikipedias, we observed a -1.9 percentage point (11.5% → 9.6%; 16.80% decrease) in the revert rate for new topic tool edits made by Junior Contributors compared to edits made using the previous add section link. In addition to increasing the likelihood of a Junior Contributor saving a new topic, the new topic tool also appears to reduce the number of errors in the published new topic that might lead to the new topic being reverted. \n", "\n", "Note again that there is high level of uncertaininty in the revert rate identified for the exisisting add new section link as indicated by the red error bar in the chart above. This is due to the smaller numer of events available to review for this editing method. As a result, there is not sufficient evidence to identify any significant changes in revert rate caused by the new topic tool. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Revert rate by participating Wikipedia" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# aggregrate data by wiki and editing interface\n", "new_topic_reverts_jc_bywiki <- new_topic_reverts_jc %>%\n", " group_by(wiki, section_edit_type) %>%\n", " summarise(total_reverts = n_distinct(revision_id[is_reverted == \"Reverted\"]),\n", " total_comments = n_distinct(revision_id),\n", " revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop') %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior Contributors new topic revert rate by participating Wikipedia
WikipediaEditing method1Number of new topics revertedNumber of new topics publishedRevert rate2CI (Lower Bound)3CI (Upper Bound)3
Amharic WikipediaNew topic tool010 %0.000.77
Bengali WikipediaExisting add new section link020 %0.000.57
Bengali WikipediaNew topic tool52520 %0.070.37
Chinese WikipediaExisting add new section link1119.09 %0.000.31
Chinese WikipediaNew topic tool7828.54 %0.030.15
Dutch WikipediaExisting add new section link1333.33 %0.010.77
Dutch WikipediaNew topic tool3644.69 %0.010.11
Egyptian WikipediaExisting add new section link11100 %0.231.00
Egyptian WikipediaNew topic tool1812.5 %0.000.40
French WikipediaExisting add new section link43411.76 %0.030.24
French WikipediaNew topic tool404449.01 %0.070.12
Hebrew WikipediaExisting add new section link060 %0.000.26
Hebrew WikipediaNew topic tool1971.03 %0.000.04
Hindi WikipediaExisting add new section link1250 %0.060.94
Hindi WikipediaNew topic tool42020 %0.060.39
Indonesian WikipediaExisting add new section link11010 %0.000.33
Indonesian WikipediaNew topic tool165927.12 %0.170.39
Italian WikipediaExisting add new section link21811.11 %0.010.28
Italian WikipediaNew topic tool273238.36 %0.060.12
Japanese WikipediaExisting add new section link2922.22 %0.030.50
Japanese WikipediaNew topic tool131847.07 %0.040.11
Korean WikipediaExisting add new section link070 %0.000.23
Korean WikipediaNew topic tool83622.22 %0.100.36
Persian WikipediaExisting add new section link0320 %0.000.06
Persian WikipediaNew topic tool21701.18 %0.000.03
Polish WikipediaExisting add new section link070 %0.000.23
Polish WikipediaNew topic tool107812.82 %0.060.21
Portuguese WikipediaExisting add new section link030 %0.000.44
Portuguese WikipediaNew topic tool119411.7 %0.060.19
Spanish WikipediaExisting add new section link51631.25 %0.120.54
Spanish WikipediaNew topic tool363679.81 %0.070.13
Thai WikipediaNew topic tool1156.67 %0.000.23
Ukrainian WikipediaNew topic tool114325.58 %0.140.39
Vietnamese WikipediaExisting add new section link21315.38 %0.020.37
Vietnamese WikipediaNew topic tool115520 %0.110.31
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Defined as percent of new topics reverted within 48 hours.\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_reverts_jc_bywiki_table <- new_topic_reverts_jc_bywiki %>%\n", " select(c(1,2,3,4,5,12,13)) %>% #remove unneeded rows\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior Contributors new topic revert rate by participating Wikipedia\"\n", " ) %>%\n", " cols_label(\n", " wiki = \"Wikipedia\",\n", " section_edit_type = \"Editing method\",\n", " total_reverts = \"Number of new topics reverted\",\n", " total_comments = \"Number of new topics published\",\n", " revert_rate = \"Revert rate\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of new topics reverted within 48 hours.\",\n", " locations = cells_column_labels(\n", " columns = 'revert_rate'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_reverts_jc_bywiki_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_reverts_jc_bywiki_table.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some per participating Wikipedia trend highlights :\n", "\n", "* Revert rates vary on a per wiki basis. 7 out of the 20 participating wikipedias had higher new topic tool revert rates, with significantly high new topic tool revert rates observed for Bengali (20%), Indonesian (27%) and Korean (22%).\n", "* Much of this variation is due to the signficant difference in number of events logged for each new topic edit type. Since new topic tool events were sampled at 100% and non-new topic tool events were sampled at 6.25%, some of the smaller wikis had very few number of non new topic tool events logged to obtain a representative sample. As a result, there is a lot of uncertainty around the identifed revert rates as indicated by the 95% credible intervals. \n", "* Additional data would be needed to confirm any per wiki revert rate differences especially for smaller wikis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Revert rate by experience level\n", "\n", "To understand the impact of experience level on revert rate, we reviewed the revert rates for non-Junior Contriburtors as well using the same edit segments used for the new topic completion rate analysis." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# divide experience level groups\n", "new_topic_reverts_exp <- new_topic_reverts %>%\n", " mutate(experience_group = cut(as.numeric(experience_level), \n", " breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,\n", " Inf), \n", " labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',\n", " 'over 3000 edits'), include.lowest = TRUE))\n", "\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# aggregate data based on editor and experience level\n", "new_topic_reverts_byexp <- new_topic_reverts_exp %>%\n", " group_by(experience_group, section_edit_type) %>%\n", " summarise(total_reverts = n_distinct(revision_id[is_reverted == \"Reverted\"]),\n", " total_comments = n_distinct(revision_id),\n", " revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop') %>%\n", " ungroup() %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Contributors new topic revert rate by experience level
Experience level1Editing method2Number of new topics revertedNumber of new topics publishedRevert rate3CI (Lower Bound)4CI (Upper Bound)4
0-100 editsExisting add new section link2017511.43 %0.070.16
0-100 editsNew topic tool20721679.55 %0.080.11
101-500 editsExisting add new section link1502 %0.000.08
101-500 editsNew topic tool409264.32 %0.030.06
501-1000 editsExisting add new section link1362.78 %0.000.10
501-1000 editsNew topic tool64681.28 %0.000.02
1001-1500 editsExisting add new section link1119.09 %0.000.31
1001-1500 editsNew topic tool92903.1 %0.010.05
1501-2000 editsExisting add new section link0170 %0.000.11
1501-2000 editsNew topic tool73472.02 %0.010.04
2001-2500 editsExisting add new section link0290 %0.000.06
2001-2500 editsNew topic tool22550.78 %0.000.02
2501-3000 editsExisting add new section link0100 %0.000.17
2501-3000 editsNew topic tool41652.42 %0.010.05
over 3000 editsExisting add new section link139581.36 %0.010.02
over 3000 editsNew topic tool9148941.86 %0.010.02
\n", "

\n", " \n", " 1\n", " \n", " \n", " Junior contributor defined as having under cumulative 100 edits. Non-Junior Contributor is defined as having over 100 cumulative edits\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " Defined as percent of new topics reverted within 48 hours.\n", "
\n", "

\n", "

\n", " \n", " 4\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_reverts_byexp_table <- new_topic_reverts_byexp %>%\n", " select(c(1,2,3,4,5,12,13)) %>% #remove unneeded rows\n", " gt() %>%\n", " tab_header(\n", " title = \"Contributors new topic revert rate by experience level\"\n", " ) %>%\n", " cols_label(\n", " experience_group = \"Experience level\",\n", " section_edit_type = \"Editing method\",\n", " total_reverts = \"Number of new topics reverted\",\n", " total_comments = \"Number of new topics published\",\n", " revert_rate = \"Revert rate\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of new topics reverted within 48 hours.\",\n", " locations = cells_column_labels(\n", " columns = 'revert_rate'\n", " )\n", " )%>%\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Junior contributor defined as having under cumulative 100 edits. Non-Junior Contributor is defined as having over 100 cumulative edits\",\n", " locations = cells_column_labels(\n", " columns = 'experience_group'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_reverts_byexp_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_reverts_byexp_table.html\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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UKiHEjwMxWn9oVGLDLMPa6Xqj9rbLcMki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\n7rQ3xeb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dLpL0eF6ImpNe+vf9dluPdX+CKdXqfuLHT01yFfu7Zyfe9St+7lKOK/l0f/mXP3p1F4OX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\nI7ydga2XJp07TUQiu02v8h/wyUtaUuf8633EMESklrOPL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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# plot reverts by experience\n", "\n", "p <- new_topic_reverts_byexp %>%\n", " ggplot(aes(x= section_edit_type , y = total_reverts/total_comments, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', alpha = 0.5, size = 1, position = dodge, width = 0.25) +\n", " geom_text(aes(label = paste(revert_rate),fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " scale_y_continuous(labels = scales::percent) +\n", " facet_wrap(~ experience_group, scale = \"free_y\") +\n", " labs (y = \"Percent of new topics reverted\",\n", " title = \"Contributor new topic revert rate by experience level \\n across all participating Wikipedias\",\n", " caption = \"Red error bars: 95% credible intervals\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\"), name = \"Editing Method\", labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "p\n", "\n", "ggsave(\"Figures/new_topic_reverts_byexp.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see a lower new topic tool revert rate for all experience groups under 1500 edits, with the exception fo the 101-500 edit count group where there see an increase in revert rate for editors with between 101-500 edits. \n", "\n", "Unfortunately, there is not sufficient data to confidently compare these revert rates within these more granular edit count segments as there are very few events logged for users at higher experience levels, especially for the existing add new section link editing method. The low number of events logged creates more uncertainty in the identifed revert rates as indicated by the large error bars.\n", "\n", "Excluding the edit groups where the sample sizes are too small, there was a slight increase in reverts for the over 3000 edits group (1.36% to 1.86%)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Blocked New Topic Users\n", "### Overall percent of Junior Contributors blocked after posting a new topic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also reviewed the number of Junior Contributors blocked after posting a new topic using either the new topic tool or previous add new section link.\n", "\n", "Data comes from the [mediawiki_user_history](https://wikitech.wikimedia.org/wiki/Analytics/Data_Lake/Edits/Mediawiki_user_history) table. All block events were identified in the data by `caused_by_event_type = \"alterblocks\"`. The data includes any Contributors that were blocked after posting a comment; however, we do not know if they were blocked specifically due to the comment posted. Data is also currently limited to dates of the AB test. Some new topic users may have been blocked following this analysis. Similar to the revert rate methodology, we joined this data with AB test data logged in editattemptstep and talk_page_edit to isolate data to the attempts included in the AB test and try to exclude any non new topic edits.\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "new_topic_blocks <-\n", " read.csv(\n", " file = 'Data/new_topic_blocks.csv',\n", " header = TRUE,\n", " sep = \",\",\n", " stringsAsFactors = FALSE\n", " ) # loads all revert data" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "#clarfiy levels and lables for factor variables\n", "new_topic_blocks$section_edit_type <-\n", " factor(\n", " new_topic_blocks$section_edit_type,\n", " levels = c(\"non-new-topic-tool\", \"new-topic-tool\"),\n", " labels = c(\"Existing add new section link\", \"New topic tool\")\n", " )\n", "\n", "#clarfiy wiki names\n", "new_topic_blocks<- new_topic_blocks%>%\n", " mutate(\n", " wiki = case_when(\n", " #clarfiy participating project names\n", " wiki == 'amwiki' ~ \"Amharic Wikipedia\",\n", " wiki == 'bnwiki' ~ \"Bengali Wikipedia\",\n", " wiki == 'zhwiki' ~ \"Chinese Wikipedia\",\n", " wiki == 'nlwiki' ~ 'Dutch Wikipedia',\n", " wiki == 'arzwiki' ~ 'Egyptian Wikipedia',\n", " wiki == 'frwiki' ~ 'French Wikipedia',\n", " wiki == 'hewiki' ~ 'Hebrew Wikipedia',\n", " wiki == 'hiwiki' ~ 'Hindi Wikipedia',\n", " wiki == 'idwiki' ~ 'Indonesian Wikipedia',\n", " wiki == 'itwiki' ~ 'Italian Wikipedia', \n", " wiki == 'jawiki' ~ 'Japanese Wikipedia', \n", " wiki == 'kowiki' ~ 'Korean Wikipedia',\n", " wiki == 'omwiki' ~ 'Oromo Wikipedia', \n", " wiki == 'fawiki' ~ 'Persian Wikipedia', \n", " wiki == 'plwiki' ~ 'Polish Wikipedia', \n", " wiki == 'ptwiki' ~ 'Portuguese Wikipedia',\n", " wiki == 'eswiki' ~ 'Spanish Wikipedia',\n", " wiki == 'thwiki' ~ 'Thai Wikipedia', \n", " wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',\n", " wiki == 'viwiki' ~ 'Vietnamese Wikipedia', \n", " )\n", " ) " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Create new column to identify Junior and Non-Junior Contributors\n", "\n", "new_topic_blocks <- new_topic_blocks %>%\n", " mutate(\n", " is_junior = case_when(\n", " #clarfiy participating project names\n", " experience_level < 100 ~ \"Junior Contributor\",\n", " experience_level >= 100 ~ \"Non-Junior Contributor\" \n", " ),\n", " is_junior = factor(is_junior,\n", " levels = c(\"Non-Junior Contributor\", \"Junior Contributor\")\n", " ))\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# filter date to only look at junior contributors blocked\n", "new_topic_blocks_jc <- new_topic_blocks %>%\n", " filter(is_junior == 'Junior Contributor') " ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "# blocks for JCs across all wikis\n", "new_topic_blocks_jc_all <- new_topic_blocks_jc %>%\n", " group_by(section_edit_type) %>%\n", " summarise(total_blocked_users = sum(blocked_user),\n", " total_users = sum(all_users),\n", " pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), \"%\"), \n", " .groups = 'drop') %>%\n", " ungroup() %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior contributors blocked after publishing a new topic
Editing method1Number of users blockedNumber of users that published a new topicPercent of users blocked2CI (Lower Bound)3CI (Upper Bound)3
Existing add new section link71694.14 %0.020.08
New topic tool2914651.98 %0.010.03
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Percent of junior contributors blocked after posting a new topic during the AB test\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_blocks_jc_all_table <- new_topic_blocks_jc_all %>%\n", " select(1,2,3,4, 11, 12) %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior contributors blocked after publishing a new topic \"\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing method\",\n", " total_blocked_users = \"Number of users blocked\",\n", " total_users = \"Number of users that published a new topic\",\n", " pct_blocked = \"Percent of users blocked\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Percent of junior contributors blocked after posting a new topic during the AB test\",\n", " locations = cells_column_labels(\n", " columns = 'pct_blocked'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_blocks_jc_all_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_blocks_jc_all_table.html\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Overall, across all participating Wikipedias, there was a -228 percentage points (4.14% → 1.98%; 52.17% decrease) in the percent of Junior Contributors blocked after posting a new topic using the new topic tool compared to the Junior Contributors using the existing add new section link. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Junior Contributors blocked after posting a new topic by participating Wikipedia" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# blocked users by wiki\n", "new_topic_blocks_jc_bywiki <- new_topic_blocks_jc %>%\n", " group_by(wiki, section_edit_type) %>%\n", " summarise(total_blocked_users = sum(blocked_user),\n", " total_users = sum(all_users),\n", " pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), \"%\"), \n", " .groups = 'drop') %>%\n", " ungroup() %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior Contributors blocked after publishing a new topic by participating Wikipedia
WikipediaEditing method1Number of users blockedNumber of users that published a new topicPercent of users blocked2CI (Lower Bound)3CI (Upper Bound)3
Amharic WikipediaNew topic tool010 %0.000.77
Bengali WikipediaExisting add new section link020 %0.000.57
Bengali WikipediaNew topic tool1156.67 %0.000.23
Chinese WikipediaExisting add new section link0110 %0.000.16
Chinese WikipediaNew topic tool1651.54 %0.000.06
Dutch WikipediaExisting add new section link030 %0.000.44
Dutch WikipediaNew topic tool0560 %0.000.03
Egyptian WikipediaExisting add new section link010 %0.000.77
Egyptian WikipediaNew topic tool080 %0.000.21
French WikipediaExisting add new section link1333.03 %0.000.11
French WikipediaNew topic tool33011 %0.000.02
Hebrew WikipediaExisting add new section link060 %0.000.26
Hebrew WikipediaNew topic tool2712.82 %0.000.08
Hindi WikipediaExisting add new section link020 %0.000.57
Hindi WikipediaNew topic tool0170 %0.000.11
Indonesian WikipediaExisting add new section link11010 %0.000.33
Indonesian WikipediaNew topic tool3466.52 %0.010.15
Italian WikipediaExisting add new section link21811.11 %0.010.28
Italian WikipediaNew topic tool51942.58 %0.010.05
Japanese WikipediaExisting add new section link1714.29 %0.000.44
Japanese WikipediaNew topic tool01230 %0.000.02
Korean WikipediaExisting add new section link1520 %0.000.56
Korean WikipediaNew topic tool1214.76 %0.000.17
Persian WikipediaExisting add new section link0320 %0.000.06
Persian WikipediaNew topic tool4964.17 %0.010.09
Polish WikipediaExisting add new section link070 %0.000.23
Polish WikipediaNew topic tool3585.17 %0.010.12
Portuguese WikipediaExisting add new section link030 %0.000.44
Portuguese WikipediaNew topic tool2762.63 %0.000.07
Spanish WikipediaExisting add new section link0160 %0.000.11
Spanish WikipediaNew topic tool42271.76 %0.000.04
Thai WikipediaNew topic tool0120 %0.000.15
Ukrainian WikipediaNew topic tool0370 %0.000.05
Vietnamese WikipediaExisting add new section link1137.69 %0.000.26
Vietnamese WikipediaNew topic tool0410 %0.000.05
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Percent of junior contributors blocked after posting a comment during the AB test\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_blocks_jc_bywiki_table <- new_topic_blocks_jc_bywiki %>%\n", " select(1,2,3,4,5,12,13) %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior Contributors blocked after publishing a new topic by participating Wikipedia \"\n", " ) %>%\n", " cols_label(\n", " wiki = \"Wikipedia\",\n", " section_edit_type= \"Editing method\",\n", " total_blocked_users = \"Number of users blocked\",\n", " total_users = \"Number of users that published a new topic\",\n", " pct_blocked = \"Percent of users blocked\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", "\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Percent of junior contributors blocked after posting a comment during the AB test\",\n", " locations = cells_column_labels(\n", " columns = 'pct_blocked'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_blocks_jc_bywiki_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_blocks_jc_bywiki_table.html\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a per Wikipedia basis, under 5% of all Junior Contributors that used the new topic tool were blocked with the exception of Bengali Wikipedia (6.67%), Polish Wikipedia (5.17%) and Indonesian Wikipedia (6.52%). Note Bengali Wikipedia only had 15 distinct Junior Contributors that used the new topic tool during the AB test, which is small and likely not a representative sample. \n", "\n", "Please refer to the 95% credible intervals in the table above to see the level of uncertainty associated with each of these values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blocked contributors overall by experience level\n", "\n", "To understand the impact of experience level on blocks, we reviewed the number of blocks for non-Junior contributors as well using the same edit segments used for the new topic completion rate and revert rate analyses above." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# divide experience level groups\n", "new_topic_blocks_exp <- new_topic_blocks %>%\n", " mutate(experience_group = cut(as.numeric(experience_level), \n", " breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,\n", " Inf), \n", " labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',\n", " 'over 3000 edits'), include.lowest = TRUE))\n" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "new_topic_blocks_byexp <- new_topic_blocks_exp %>%\n", " group_by(section_edit_type, experience_group) %>%\n", " summarise(total_blocked_users = sum(blocked_user),\n", " total_users = sum(all_users),\n", " pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), \"%\")\n", " , .groups = 'drop') %>%\n", " ungroup() %>%\n", " cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%\n", " mutate(lower = round(lower,2), \n", " upper = round(upper, 2))\n" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Contributors blocked after publishing a new topic by experience level
Editing method1Experience levelNumber of users blockedNumber of users that saved a new topicPercent of users blocked2CI (Lower Bound)3CI (Upper Bound)3
Existing add new section link0-100 edits71704.12 %0.020.07
Existing add new section link101-500 edits1432.33 %0.000.09
Existing add new section link501-1000 edits1234.35 %0.000.16
Existing add new section link1001-1500 edits080 %0.000.21
Existing add new section link1501-2000 edits0100 %0.000.17
Existing add new section link2001-2500 edits0130 %0.000.13
Existing add new section link2501-3000 edits030 %0.000.44
Existing add new section linkover 3000 edits63681.63 %0.010.03
New topic tool0-100 edits2914661.98 %0.010.03
New topic tool101-500 edits83562.25 %0.010.04
New topic tool501-1000 edits41203.33 %0.010.07
New topic tool1001-1500 edits0660 %0.000.03
New topic tool1501-2000 edits2712.82 %0.000.08
New topic tool2001-2500 edits1531.89 %0.000.07
New topic tool2501-3000 edits1333.03 %0.000.11
New topic toolover 3000 edits117731.42 %0.010.02
\n", "

\n", " \n", " 1\n", " \n", " \n", " Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool event\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Percent of contributors blocked after posting a new topic during the AB test\n", "
\n", "

\n", "

\n", " \n", " 3\n", " \n", " \n", " 95% credible intervals. There is a 95% probability that the parameter lies in this interval\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_topic_blocks_byexp_table <- new_topic_blocks_byexp %>%\n", " select(1,2,3,4,5, 12,13) %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Contributors blocked after publishing a new topic by experience level\",\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing method\",\n", " experience_group = \"Experience level\",\n", " total_blocked_users = \"Number of users blocked\",\n", " total_users = \"Number of users that saved a new topic\",\n", " pct_blocked = \"Percent of users blocked\",\n", " lower = \"CI (Lower Bound)\",\n", " upper = \"CI (Upper Bound)\"\n", " ) %>%\n", "\n", " tab_footnote(\n", " footnote = \"Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool event\",\n", " locations = cells_column_labels(\n", " columns = 'section_edit_type'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Percent of contributors blocked after posting a new topic during the AB test\",\n", " locations = cells_column_labels(\n", " columns = 'pct_blocked'\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"95% credible intervals. There is a 95% probability that the parameter lies in this interval\",\n", " locations = cells_column_labels(\n", " columns = c('lower', 'upper')\n", " )\n", " ) %>%\n", " gtsave(\n", " \"new_topic_blocks_byexp_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"new_topic_blocks_byexp_table.html\")\n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "image/png": 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vAwICAgICHHtyx4sXL+bqlVdWVo5fpj8AjB8ERqHlhFIAACAASURBVAEAxuRqi7mp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\npS5evOhYB8axXoFOp3M8y2w2m0wm7mfIpkoAJ8KPXpcg9qRnm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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# plot proportion of blocks by experience levels\n", "\n", "p <- new_topic_blocks_byexp %>%\n", " ggplot(aes(x= section_edit_type, y = total_blocked_users/total_users, fill = section_edit_type,)) +\n", " geom_col(position = 'dodge') +\n", " geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', alpha = 0.5, size = 1, position = dodge, width = 0.25) +\n", " geom_text(aes(label = paste(pct_blocked), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " scale_y_continuous(labels = scales::percent) +\n", " facet_wrap(~ experience_group, scale = \"free_y\") +\n", " labs (y = \"Percent of blocked users\",\n", " x = \"Editing experience\",\n", " title = \"Contributors blocked after posting a comment by experience level\",\n", " caption = \"Red error bars: 95% credible intervals\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\"), name = \"Editing Method\", labels = c(\"Existing add new section link\", \"New topic tool\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "p\n", "\n", "ggsave(\"Figures/new_topic_blocks_byexp.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is not sufficient data to identify any signficant differences between blocked rates for these more granular editing segments. The small number of recorded blocked events especically for the existing add new section link creates a high degree of uncertainty in the identified values as representative of the actual population. \n", "\n", "For new topic tool usage (where sampling rates are higher), there is a lower level of uncertainty and the blocked rates from all editor groups are below 4% and below the identifed blocked percentages for the existing add new section link method (for groups where blocks were logged).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Curiosities\n", "\n", "We also explored if the new topic tool resulted in a greater number of Junior Contributors to start participating productively on talk pages and if it caused a greater percentage of Junior Contributors to continue participating productively on talk pages." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Number of Junior Contributors\n", "\n", "This metric was defined as the number of distinct Junior Contributors who make at least one new topic edit to a page in a talk namespace during the AB test. Since different sampling rates were applied to each editor type in the AB test, we removed any events that were oversampled (sampling rate increased to 100%) to allow us to directly compare the numbers between the two groups.\n" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "num_jc_editors <- new_topic_attempts %>%\n", " filter(is_junior == 'Junior Contributor',\n", " is_oversample == 'false' ) %>% #remove oversampled events\n", " group_by(experiment_group, section_edit_type) %>%\n", " summarise(total_users_attempt = n_distinct(user_id),\n", " total_users_complete = n_distinct(user_id[edit_success == 'Complete']), .groups = 'drop') " ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in if ((loc$groups %>% rlang::eval_tidy()) == \"title\") {:\n", "“the condition has length > 1 and only the first element will be used”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Number of Junior Contributors that made a new topic attempt during the AB test by test group and section edit type1
Test groupEditing methodNumber of users that attempted a new topicNumber of users that published a new topic
controlExisting add new section link387147
controlNew topic tool175
testExisting add new section link8426
testNew topic tool336114
\n", "

\n", " \n", " 1\n", " \n", " \n", " Based on a sampling rate of 6.25% for all events. Any oversampled events were removed so data for the two editor types could be directly compared\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_jc_editors_table <- num_jc_editors %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Number of Junior Contributors that made a new topic attempt during the AB test by test group and section edit type\"\n", " ) %>%\n", " cols_label(\n", " experiment_group = \"Test group\",\n", " section_edit_type = \"Editing method\",\n", " total_users_attempt = \"Number of users that attempted a new topic\",\n", " total_users_complete = \"Number of users that published a new topic\"\n", " ) %>% \n", " tab_row_group(\n", " rows = experiment_group == 'control'\n", " ) %>%\n", "tab_row_group(\n", " rows = experiment_group == 'test'\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Based on a sampling rate of 6.25% for all events. Any oversampled events were removed so data for the two editor types could be directly compared\",\n", " locations = cells_title(\n", " )\n", " ) %>%\n", " gtsave(\n", " \"num_jc_editors_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"num_jc_editors_table.html\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: There are Contributors for each editing method type in each AB test group. This is because contributors within each group also had the option to explicitly turn the tool on or off in their preferences; however, these contributors remained in the same group they were bucketed in for the duration of the test. See methodology section of this report. " ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "image/png": 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7e//a2iokJ/0bp165RPDoUQQ4cOVZe96xo0aJDcSJMmTXbs2GGwtKamJj09ffny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\nn/Ly8oQQ4eHh3bt3b9u2bUhISFVV1ZkzZ7Zs2XLp0qXi4uKRI0d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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "p <-num_jc_editors %>%\n", " group_by(experiment_group) %>%\n", " summarise(total_user_complete = sum(total_users_complete), .groups = 'drop') %>%\n", " ggplot(aes(x= experiment_group, y = total_user_complete, fill = experiment_group)) +\n", " geom_col(position = 'dodge') +\n", " geom_text(aes(label = paste(total_user_complete),fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " labs (y = \"Number of Junior Contributors\",\n", " x = \"Experiment group\",\n", " title = \"Number of Junior Contributors that completed a new topic by test group \\n across all participating Wikipedias\") +\n", " scale_fill_manual(values= c(\"#999999\", \"steelblue2\"), name = \"Test Group\", labels = c(\"Control\", \"Test\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "p\n", "\n", "ggsave(\"Figures/num_jc_editors_bygroup.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The new topic tool did not impact the number of Junior Contributors that completed a new topic in either experiment group. Only 2 more distinct users were recorded as making a new topic edit in the test group compared to the control group." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retention of Junior Contributors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, we looked into whether Junior Contributors that made at least one new topic edit during the time of the AB test (our cohort) returned to make another edit (including replies or other edits) on a talk namespace. Specifically, we calculated the percentage of Junior Contributors who returned to make at least one edit to a page in a talk namespace in each of the following retention windows: \n", "* 1 week after publishing a new topic (2 -8 days). Note: Since user activity naturally comes in bursts, we excluded the time (first 24 hours) immediately following the Contributors' first edit.\n", "* 2 weeks after publishing a new topic (9- 15 days)\n", "* 3 weeks after publishing a new topic (16 to 22 days)\n", "\n", "Data for this analysis came from events logged in mediawiki_history. We only reviewed comments that were not reverted within 48 hours. Due to availability of data at the time of this analysis, we also only reviewed users that made an edit within the first three weeks of the AB test (Jan 28 through March 4th).\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "query <-\n", "\"\n", "WITH first_edits AS (\n", "-- users that made an attempt during the AB Test\n", " SELECT\n", " event_user_text as user_name,\n", " wiki_db AS wiki,\n", " min(event_timestamp) as first_edit_time,\n", " CASE\n", " WHEN min(event_user_revision_count) is NULL THEN 'undefined'\n", " WHEN min(event_user_revision_count) < 100 THEN 'junior'\n", " ELSE 'non-junior'\n", " END AS experience_level,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type\n", " FROM wmf.mediawiki_history mwh\n", " JOIN event.mediawiki_talk_page_edit tpe\n", "ON mwh.revision_id = tpe.revision_id\n", "AND mwh.wiki_db = tpe.`database`\n", " WHERE\n", " snapshot = '2022-03'\n", " AND event_timestamp >= '2022-01-27' \n", " AND event_timestamp <= '2022-03-04'\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", " -- do not include new discussion tool talk page edits\n", " AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- first edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " GROUP BY event_user_text,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),\n", " wiki_db\n", ")\n", " \n", "SELECT\n", " first_edits.experience_level,\n", " first_edits.section_edit_type,\n", " (count(first_week.user_name)/count(*)) as first_week_retention_rate\n", "FROM first_edits\n", "LEFT JOIN\n", "(\n", " SELECT event_user_text as user_name,\n", " first_edits.first_edit_time,\n", " min(event_timestamp) as return_time\n", " FROM wmf.mediawiki_history mh\n", " INNER JOIN first_edits\n", " ON mh.event_user_text = first_edits.user_name\n", " WHERE\n", " snapshot = '2022-03'\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- on all participating wikis\n", " AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',\n", " 'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',\n", " 'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')\n", " -- return edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " AND first_edits.first_edit_time >= '2022-01-27' \n", " AND first_edits.first_edit_time <= '2022-03-04'\n", " -- second revision is between two and 8 days\n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (2*24*60*60)) \n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (8*24*60*60))\n", " GROUP BY event_user_text, \n", " first_edits.first_edit_time \n", ") AS first_week\n", "ON \n", "(first_edits.user_name = first_week.user_name and\n", "first_edits.first_edit_time = first_week.first_edit_time \n", ")\n", "GROUP BY\n", " first_edits.experience_level,\n", " first_edits.section_edit_type;\n", "\"" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Don't forget to authenticate with Kerberos using kinit\n", "\n" ] } ], "source": [ "week_one_retention <- wmfdata::query_hive(query)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "# find week 2 retention\n", "\n", "query <-\n", "\"\n", "WITH first_edits AS (\n", "-- users that made an attempt during the AB Test\n", " SELECT\n", " event_user_text as user_name,\n", " wiki_db AS wiki,\n", " min(event_timestamp) as first_edit_time,\n", " CASE\n", " WHEN min(event_user_revision_count) is NULL THEN 'undefined'\n", " WHEN min(event_user_revision_count) < 100 THEN 'junior'\n", " ELSE 'non-junior'\n", " END AS experience_level,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type\n", " FROM wmf.mediawiki_history mwh\n", " JOIN event.mediawiki_talk_page_edit tpe\n", "ON mwh.revision_id = tpe.revision_id\n", "AND mwh.wiki_db = tpe.`database`\n", " WHERE\n", " snapshot = '2022-03'\n", " AND event_timestamp >= '2022-01-27' \n", " AND event_timestamp <= '2022-03-04'\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", " -- do not include new discussion tool talk page edits\n", " AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- first edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " GROUP BY event_user_text,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),\n", " wiki_db\n", ")\n", " \n", "SELECT\n", " first_edits.experience_level,\n", " first_edits.section_edit_type,\n", " (count(first_week.user_name)/count(*)) as two_week_retention_rate\n", "FROM first_edits\n", "LEFT JOIN\n", "(\n", " SELECT event_user_text as user_name,\n", " first_edits.first_edit_time,\n", " min(event_timestamp) as return_time\n", " FROM wmf.mediawiki_history mh\n", " INNER JOIN first_edits\n", " ON mh.event_user_text = first_edits.user_name\n", " WHERE\n", " snapshot = '2022-03'\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- on all participating wikis\n", " AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',\n", " 'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',\n", " 'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')\n", " -- return edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " AND first_edits.first_edit_time >= '2022-01-27' \n", " AND first_edits.first_edit_time <= '2022-03-04'\n", " -- second revision is between two and 8 days\n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (9*24*60*60)) \n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (15*24*60*60))\n", " GROUP BY event_user_text, \n", " first_edits.first_edit_time \n", ") AS first_week\n", "ON \n", "(first_edits.user_name = first_week.user_name and\n", "first_edits.first_edit_time = first_week.first_edit_time \n", ")\n", "GROUP BY\n", " first_edits.experience_level,\n", " first_edits.section_edit_type;\n", "\"" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Don't forget to authenticate with Kerberos using kinit\n", "\n" ] } ], "source": [ "week_two_retention <- wmfdata::query_hive(query)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 3
experience_levelsection_edit_typetwo_week_retention_rate
<chr><chr><dbl>
junior new-topic-tool 0.1179916
junior non-newtopic-tool0.1098266
non-juniornew-topic-tool 0.5279441
non-juniornon-newtopic-tool0.7252221
\n" ], "text/latex": [ "A data.frame: 4 × 3\n", "\\begin{tabular}{lll}\n", " experience\\_level & section\\_edit\\_type & two\\_week\\_retention\\_rate\\\\\n", " & & \\\\\n", "\\hline\n", "\t junior & new-topic-tool & 0.1179916\\\\\n", "\t junior & non-newtopic-tool & 0.1098266\\\\\n", "\t non-junior & new-topic-tool & 0.5279441\\\\\n", "\t non-junior & non-newtopic-tool & 0.7252221\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 3\n", "\n", "| experience_level <chr> | section_edit_type <chr> | two_week_retention_rate <dbl> |\n", "|---|---|---|\n", "| junior | new-topic-tool | 0.1179916 |\n", "| junior | non-newtopic-tool | 0.1098266 |\n", "| non-junior | new-topic-tool | 0.5279441 |\n", "| non-junior | non-newtopic-tool | 0.7252221 |\n", "\n" ], "text/plain": [ " experience_level section_edit_type two_week_retention_rate\n", "1 junior new-topic-tool 0.1179916 \n", "2 junior non-newtopic-tool 0.1098266 \n", "3 non-junior new-topic-tool 0.5279441 \n", "4 non-junior non-newtopic-tool 0.7252221 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "week_two_retention" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "# find week 3 retention\n", "\n", "query <-\n", "\"\n", "WITH first_edits AS (\n", "-- users that made an attempt during the AB Test\n", " SELECT\n", " event_user_text as user_name,\n", " wiki_db AS wiki,\n", " min(event_timestamp) as first_edit_time,\n", " CASE\n", " WHEN min(event_user_revision_count) is NULL THEN 'undefined'\n", " WHEN min(event_user_revision_count) < 100 THEN 'junior'\n", " ELSE 'non-junior'\n", " END AS experience_level,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type\n", " FROM wmf.mediawiki_history mwh\n", " JOIN event.mediawiki_talk_page_edit tpe\n", "ON mwh.revision_id = tpe.revision_id\n", "AND mwh.wiki_db = tpe.`database`\n", " WHERE\n", " snapshot = '2022-03'\n", " AND event_timestamp >= '2022-01-27' \n", " AND event_timestamp <= '2022-03-04'\n", " AND ((month = 01 and day >= 27) OR (month = 02) OR\n", " (month = 03 and day <= 25))\n", " AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',\n", " 'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',\n", " 'ukwiki', 'viwiki')\n", " -- do not include new discussion tool talk page edits\n", " AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- first edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " GROUP BY event_user_text,\n", " IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),\n", " wiki_db\n", ")\n", " \n", "SELECT\n", " first_edits.experience_level,\n", " first_edits.section_edit_type,\n", " (count(first_week.user_name)/count(*)) as third_week_retention_rate\n", "FROM first_edits\n", "LEFT JOIN\n", "(\n", " SELECT event_user_text as user_name,\n", " first_edits.first_edit_time,\n", " min(event_timestamp) as return_time\n", " FROM wmf.mediawiki_history mh\n", " INNER JOIN first_edits\n", " ON mh.event_user_text = first_edits.user_name\n", " WHERE\n", " snapshot = '2022-03'\n", " -- include only desktop edits\n", " AND NOT array_contains(revision_tags, 'iOS')\n", " AND NOT array_contains(revision_tags, 'Android')\n", " AND NOT array_contains(revision_tags, 'Mobile Web')\n", " -- find all edits on talk pages\n", " AND page_namespace_historical % 2 = 1\n", " AND event_entity = 'revision'\n", " AND event_type = 'create'\n", " -- on all participating wikis\n", " AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',\n", " 'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',\n", " 'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')\n", " -- return edit not reverted within 48 hours\n", " AND NOT (revision_is_identity_reverted AND \n", " revision_seconds_to_identity_revert <= 172800) -- 48 hours\n", " -- user is not a bot and not anonymous\n", " AND SIZE(event_user_is_bot_by_historical) = 0 \n", " AND SIZE(event_user_is_bot_by) = 0\n", " AND event_user_is_anonymous = FALSE\n", " AND first_edits.first_edit_time >= '2022-01-27' \n", " AND first_edits.first_edit_time <= '2022-03-04'\n", " -- second revision is between two and 8 days\n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (16*24*60*60)) \n", " AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=\n", " (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (22*24*60*60))\n", " GROUP BY event_user_text, \n", " first_edits.first_edit_time \n", ") AS first_week\n", "ON \n", "(first_edits.user_name = first_week.user_name and\n", "first_edits.first_edit_time = first_week.first_edit_time \n", ")\n", "GROUP BY\n", " first_edits.experience_level,\n", " first_edits.section_edit_type;\n", "\"" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Don't forget to authenticate with Kerberos using kinit\n", "\n" ] } ], "source": [ "week_three_retention <- wmfdata::query_hive(query)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "# Join all the data\n", "\n", "retention_rates_all <- merge(week_one_retention, week_two_retention) %>%\n", " merge(week_three_retention)\n" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "#clarify levels and labels for factor variables\n", "retention_rates_all$section_edit_type <-\n", " factor(\n", " retention_rates_all$section_edit_type,\n", " levels = c(\"non-newtopic-tool\", \"new-topic-tool\"),\n", " labels = c(\"Previous add new section link\", \"New topic tool\")\n", " )\n", "\n", "retention_rates_all$experience_level <-\n", " factor(retention_rates_all$experience_level,\n", " levels = c( \"non-junior\", \"junior\"),\n", " labels = c(\"Non-Junior Contributor\", \"Junior Contributor\")\n", " )" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`summarise()` ungrouping output (override with `.groups` argument)\n", "\n", "Warning message in if ((loc$groups %>% rlang::eval_tidy()) == \"title\") {:\n", "“the condition has length > 1 and only the first element will be used”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Junior contributors retention rate1
Editing methodWeek 1 (2-8 days)2Week 2 (9-15 days)2Week 3 (16-22 days)2
Previous add new section link20.22 %10.67 %8.99 %
New topic tool18.54 %11.8 %8.31 %
\n", "

\n", " \n", " 1\n", " \n", " \n", " Defined as percent of Junior contributors that completed a new topic edit during the AB test and returned to make another edit\n", "
\n", "

\n", "

\n", " \n", " 2\n", " \n", " \n", " Defined as days since first new topic edit during the AB test.\n", "
\n", "

\n", "
\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Restrict to just Junior Contributors\n", "retention_rates_jc_table <- retention_rates_all %>%\n", " filter(experience_level == 'Junior Contributor') %>%\n", " group_by(section_edit_type) %>%\n", " summarise(week_1 = paste(round((first_week_retention_rate * 100), 2), \"%\"),\n", " week_2= paste(round((two_week_retention_rate * 100), 2), \"%\"),\n", " week_3 = paste(round((third_week_retention_rate * 100), 2), \"%\")) %>%\n", " gt() %>%\n", " tab_header(\n", " title = \"Junior contributors retention rate\"\n", " ) %>%\n", " cols_label(\n", " section_edit_type = \"Editing method\",\n", " week_1 = \"Week 1 (2-8 days)\",\n", " week_2 = \"Week 2 (9-15 days)\",\n", " week_3 = \"Week 3 (16-22 days)\", \n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as percent of Junior contributors that completed a new topic edit during the AB test and returned to make another edit\",\n", " locations = cells_title(\n", " )\n", " ) %>%\n", " tab_footnote(\n", " footnote = \"Defined as days since first new topic edit during the AB test.\",\n", " locations = cells_column_labels(\n", " columns = c('week_1', 'week_2', 'week_3'))\n", " ) %>%\n", " gtsave(\n", " \"retention_rates_jc_table.html\", inline_css = TRUE)\n", "\n", "IRdisplay::display_html(file = \"retention_rates_jc_table.html\")" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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s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\nP7333q7u7ZX53Nz0R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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Plot retention rates \n", "p <- retention_rates_all %>%\n", " filter(experience_level == 'Junior Contributor') %>%\n", " gather(\"week\", \"retention_rate\", 3:5) %>%\n", " mutate(week = factor(week,\n", " levels = c(\"first_week_retention_rate\", \"two_week_retention_rate\", \"third_week_retention_rate\"),\n", " labels = c(\"week 1\", \"week 2\", \"week_3\"))) %>%\n", " ggplot(aes(x= section_edit_type, y = retention_rate*100, fill = section_edit_type)) +\n", " geom_col(position = 'dodge') +\n", " geom_text(aes(label = paste(round(retention_rate*100, 2), \"%\"), fontface=2), vjust=1.2, size = 8, color = \"white\") +\n", " facet_wrap(~ week) +\n", " scale_y_continuous() +\n", " labs (y = \"Percent of Junior Contributors \",\n", " title = \"Junior contributors new topic tool retention rate\",\n", " caption = \"Defined as the percent of Junior contributors that made at least one new topic edit \\n during the time of the AB test and returned to made another edit on a talk namespace.\") +\n", " theme_bw() +\n", " scale_fill_manual(values= c(\"#999999\", \"#000099\"), name = \"Editing Method\", labels = c(\"Previous add new section link\", \"New topic tool\")) +\n", " theme(\n", " panel.grid.minor = element_blank(),\n", " panel.background = element_blank(),\n", " plot.title = element_text(hjust = 0.5),\n", " text = element_text(size=16),\n", " legend.position=\"bottom\",\n", " axis.text.x = element_blank(),\n", " axis.title.x=element_blank(),\n", " axis.line = element_line(colour = \"black\")) \n", "\n", "p\n", "ggsave(\"Figures/jc_retention_rate.png\", p, width = 16, height = 8, units = \"in\", dpi = 300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is not a lot of variation between the retention rates observed for the existing add new section link and the new topic tool. We see the highest percentage of Junior Contributors return one week after making an edit with either new topic editing type with similar decreases during week 2 and week 3. \n", "\n", "There is a slightly lower percentage of new topic tool users that return 1 week after making an edit and slightly higher percentage that return two weeks after making an edit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steps for future investigation\n", "\n", "- Explore other methods to investigate the impact of experience level on the differing completion and revert rates.\n", "- Consider potential impacts of IP blocking, Russia invasion of Ukraine and Growth Experiments while evaluating results.\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 4 }