![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "markdown",
"source": [
"![](\"https://raw.githubusercontent.com/chrdrn/digital-behavioral-data/main/img/dbd_hex.png\")
\n",
"For reasons of research ethics and out of respect for privacy the data collected and processed in the course will be managed in a private OSF repository. Students will only have access to this OSF repository for a limited period of time.
\n", "Although the collection of data is (at least) partially documented in the showcases, detailed instructions can be found in the slides of course for the respective session.
\n", "Link: https://chrdrn.github.io/digital-behavioral-data/\n", "
\n", "\n", "![](\"https://icons.getbootstrap.com/assets/icons/info-circle-fill.svg\"){kind=icon}
Technical note\n",
"\n",
"While the chunk outputs were saved, the data basis was not. For an error-free execution of this notebook, the data must be collected and reloaded. All chunks for which the path to the data must be re-entered are marked with the following symobl: ![](\"https://icons.getbootstrap.com/assets/icons/database-fill-down.svg\"){kind=icon}
\n",
"\n",
"---"
],
"metadata": {
"id": "B-CL6Z6k8jag"
}
},
{
"cell_type": "markdown",
"source": [
"# Background\n",
"\n",
"Ths showcase has two different goals:\n",
"\n",
"\n",
"1. Exemplary presentation of the collection of [![](\"https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/reddit.svg\")
](https://www.reddit.com/) [Reddit](https://www.reddit.com/) data using the [`RedditExtractor`](https://github.com/ivan-rivera/RedditExtractor) package\n",
"2. Importing and analyzing the data collected with the 🐈🐈 [**4CAT**](https://github.com/digitalmethodsinitiative/4cat) 🐈🐈 tool. \n"
],
"metadata": {
"id": "6Zw3Q-FA-FME"
}
},
{
"cell_type": "markdown",
"source": [
"# Preparation\n",
"\n",
"Install addtional necessary packages\n",
"\n",
"⚠ It might take a few minutes to install all packages and dependencies\n",
"\n"
],
"metadata": {
"id": "oCFDGp0rUtAn"
}
},
{
"cell_type": "code",
"source": [
"install.packages(c(\n",
" \"RedditExtractoR\",\n",
" \"sjmisc\",\n",
" \"sjPlot\"))"
],
"metadata": {
"id": "XlTzN152Uv0K",
"outputId": "f07616b5-d0dc-483d-d953-94b1f7125a1e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Installing packages into ‘/usr/local/lib/R/site-library’\n",
"(as ‘lib’ is unspecified)\n",
"\n",
"also installing the dependencies ‘estimability’, ‘numDeriv’, ‘mvtnorm’, ‘xtable’, ‘minqa’, ‘nloptr’, ‘Rcpp’, ‘RcppEigen’, ‘emmeans’, ‘lme4’, ‘RJSONIO’, ‘insight’, ‘sjlabelled’, ‘bayestestR’, ‘datawizard’, ‘effectsize’, ‘ggeffects’, ‘parameters’, ‘performance’, ‘sjstats’\n",
"\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Data collection with `RedditExtractor`\n",
"\n",
"[Reddit Extractor](https://github.com/ivan-rivera/RedditExtractor) is an R package for extracting data out of [Reddit](https://www.reddit.com/). It allows you to:\n",
"\n",
"* find subreddits based on a search query\n",
"* find a user and their Reddit history\n",
"* find URLs to threads of interest and retrieve comments out of these threads\n"
],
"metadata": {
"id": "eTTlrhjEBgQ5"
}
},
{
"cell_type": "markdown",
"source": [
"## Example: Find subreddits\n",
"\n",
"Similar to the example from the seminar, the function `find_subreddits` identifies all subreddits that contain the keyword news either in their name or in their attributes."
],
"metadata": {
"id": "C5Vkg1h9B1vS"
}
},
{
"cell_type": "code",
"source": [
"# Load packages\n",
"library(tidyverse)\n",
"library(RedditExtractoR)\n",
"\n",
"# Get list of subreddits\n",
"news <- find_subreddits(\"news\")\n",
"\n",
"# Quick preview of the dataset\n",
"news %>% glimpse()\n",
"\n",
"# Arrange subreddits by subscribers\n",
"news %>% \n",
" arrange(-subscribers) %>% \n",
" tibble() %>% head()"
],
"metadata": {
"id": "EILQz5LuBwGP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Example: Find thread urls\n"
],
"metadata": {
"id": "hnFguFCqFOS1"
}
},
{
"cell_type": "code",
"source": [
"# Get list of top thread urls\n",
"news_top_urls <- find_thread_urls(\n",
" subreddit = \"news\", \n",
" sort_by = \"top\", \n",
" period = \"month\"\n",
")\n",
"\n",
"# Quick preview of dataset\n",
"news_top_urls %>% glimpse()\n",
"news_top_urls %>% tibble()"
],
"metadata": {
"id": "EtpMN5hgGOnO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Analysis of collected 🐈🐈 4CAT 🐈🐈 data \n"
],
"metadata": {
"id": "xoxxkYGIHQ9N"
}
},
{
"cell_type": "markdown",
"source": [
"## Data import"
],
"metadata": {
"id": "trQCMo84ikyi"
}
},
{
"cell_type": "code",
"source": [
"# load packages\n",
"library(readr)\n",
"\n",
"# get data from github\n",
"musk <- read_csv(ADD_DATA_PATH_HERE)\n",
"\n",
"musk_entities <- read_csv(ADD_DATA_PATH_HERE)\n",
"\n",
"# quick preview\n",
"musk %>% glimpse()\n",
"musk_entities %>% glimpse()\n"
],
"metadata": {
"id": "xuckrB5limkX",
"outputId": "aaea16c0-9987-47f4-d274-8d881f193d79",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[1mRows: \u001b[22m\u001b[34m4838\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m16\u001b[39m\n",
"\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
"\u001b[1mDelimiter:\u001b[22m \",\"\n",
"\u001b[31mchr\u001b[39m (7): thread_id, id, body, author, author_flair, subreddit, parent\n",
"\u001b[32mdbl\u001b[39m (2): score, unix_timestamp\n",
"\u001b[33mlgl\u001b[39m (6): subject, post_flair, domain, url, image_file, image_md5\n",
"\u001b[34mdttm\u001b[39m (1): timestamp\n",
"\n",
"\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
"\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
"\u001b[1mRows: \u001b[22m\u001b[34m3633\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
"\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
"\u001b[1mDelimiter:\u001b[22m \",\"\n",
"\u001b[31mchr\u001b[39m (2): word, entity\n",
"\u001b[32mdbl\u001b[39m (1): count\n",
"\n",
"\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
"\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Rows: 4,838\n",
"Columns: 16\n",
"$ thread_id \u001b[3m\u001b[90m