{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Are reviews autocorrelated?\n", "\n", "In in my [previous notebook](https://github.com/nolanbconaway/pitchfork-data/blob/master/notebooks/best-new-music-iid.ipynb), I evaluated whether best new music reviews occur independently of one another. I did not end up discovering anything to suggest otherwise: the number of best new music reviews per day was nicely explained by a binomial sampling model, and the probability of getting a best new music review did not change depending on whether there was a best new music the day before (or two days before, etc).\n", "\n", "But in that notebook I didn't account for individual *authors*. The authors are ostensibly the ones who apply scores and the best new music label, right? So let's rephrase the question: are reviews from each author autocorrelated? If an author applies the best new music label to an album, does that change the probability the next album will be best new music? If an author gives an especially low score to an album, will they look more favorably upon the next one?\n", "\n", "We know from lots of studies (like [this one](https://academic.oup.com/qje/article/131/3/1181/2590011/)) that decisions in other domains (e.g., baseball umpires calling stikes/balls) tend to affect one another, so it's plausible we can find similar patterns in this dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sqlite3, datetime\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from scipy.signal import savgol_filter\n", "\n", "pd.set_option('precision', 2)\n", "np.set_printoptions(precision=2)\n", "\n", "con = sqlite3.connect('../pitchfork.db')\n", "all_reviews = pd.read_sql('SELECT * FROM reviews WHERE pub_year < 2017', con)\n", "con.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cleaning...\n", "\n", "As in the previous notebook, I'm going to remove reviews from before the advent of the best new music label." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First best new music: January 14, 2003\n", "Global p(bnm): 0.0573885466854\n" ] } ], "source": [ "# convert pub_date to datetime object, get unix timestamps\n", "reviews = all_reviews.copy(deep = True) # slice to new object\n", "reviews['pub_date'] = pd.to_datetime(reviews.pub_date, format = '%Y-%m-%d')\n", "reviews['unix_time'] = reviews.pub_date.astype(np.int64, copy = True) // 10**9\n", "\n", "# find the first best new music, get rid of everything before it\n", "first_bnm = reviews.loc[reviews.best_new_music == True, 'unix_time'].min()\n", "reviews = reviews.loc[reviews.unix_time >= first_bnm]\n", "\n", "# print out date of first bnm\n", "idx = (reviews.unix_time == first_bnm) & (reviews.best_new_music == True)\n", "first_bnm_str = datetime.datetime.fromtimestamp(first_bnm)\n", "print('First best new music: ' + first_bnm_str.strftime('%B %d, %Y'))\n", "\n", "# find overall proportion of best new music\n", "proportion_bnm = np.mean(reviews.best_new_music)\n", "print('Global p(bnm): ' + str(proportion_bnm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How far apart do reviews tend to be?\n", "\n", "The strength of an autocorrelation likely depends on the frequency and interval between events. I'm just guessing, but probably autocorrelations are stronger when the events occur in close temporal succession. In [this notebook](https://github.com/nolanbconaway/pitchfork-data/blob/master/notebooks/reviewer-development.ipynb) I observed that there is a huge range in the number of reviews written by individual authors (from 1 to well over 200), but I haven't looked into the span of time between individual reviews." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 16024.00\n", "mean 18.64\n", "std 73.09\n", "min 0.00\n", "25% 3.00\n", "50% 7.00\n", "75% 16.00\n", "max 3550.00\n", "Name: days_elapsed, dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add columns for the author's review count, time since last review\n", "reviews['review_num'] = pd.Series(index = reviews.index)\n", "reviews['days_elapsed'] = pd.Series(index = reviews.index)\n", "\n", "for a, rows in reviews.copy().groupby('author'):\n", " rows_sort = rows.sort_values(by = 'unix_time')\n", " \n", " # add review number\n", " nums = np.arange(rows.shape[0], dtype = int)\n", " reviews.loc[rows_sort.index, 'review_num'] = nums\n", " \n", " # add days elapsed\n", " days_elapsed = np.zeros(nums.shape) -1\n", " for j in nums[1:]:\n", " curr = rows_sort.iloc[j]\n", " prev = rows_sort.iloc[j-1]\n", " seconds_elapsed = curr.unix_time - prev.unix_time\n", " days_elapsed[j] = seconds_elapsed / 86400.0\n", " \n", " days_elapsed[days_elapsed<0] = np.NAN\n", " reviews.loc[rows_sort.index, 'days_elapsed'] = days_elapsed \n", "\n", "reviews.days_elapsed.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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b7Vq8eLFWr16t/fv36/nnn9fGjRu1dOlSSfGV3mAwvnJ64403qr29XY8++qiOHDmiRx55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\nisfro8rqpMrqPB23AXiPUf77SdIarE4v5ZbGg/cwg5bIcB0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = reviews.days_elapsed.dropna()\n", "data = data[data<50]\n", "\n", "sns.distplot(data, bins = 50)\n", "plt.xlabel('Days Since Previous Review')\n", "plt.ylabel('Density')\n", "plt.xlim([-1,50])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "I checked it out: [Julianne Escobedo Shepherd](http://pitchfork.com/staff/julianne-escobedo-shepard/) didn't publish anything on Pitchfork between writing a review for Ciara's [The Evolution](http://pitchfork.com/reviews/albums/9759-the-evolution/) in 2007 and Solange's [A Seat At The Table](http://pitchfork.com/reviews/albums/22482-a-seat-at-the-table/) last year. Besides that pretty nutty point, most reviews are published within a week or so of the author's previous review." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Score and Best New Music Autocorrelations\n", "\n", "So the key question: how does the score or best new music status of review $n$ correlate with that of $n+1$?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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scorebest_new_music
count219.001.24e+02
mean-0.053.73e-03
std0.251.36e-01
min-0.96-3.33e-01
25%-0.18-6.15e-02
50%-0.03-3.08e-02
75%0.072.13e-02
max0.704.79e-01
\n", "
" ], "text/plain": [ " score best_new_music\n", "count 219.00 1.24e+02\n", "mean -0.05 3.73e-03\n", "std 0.25 1.36e-01\n", "min -0.96 -3.33e-01\n", "25% -0.18 -6.15e-02\n", "50% -0.03 -3.08e-02\n", "75% 0.07 2.13e-02\n", "max 0.70 4.79e-01" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def autocorrfun(x, col, lag = 1):\n", " x_sort = x.sort_values(by = 'review_num')\n", " return x_sort[col].autocorr(lag)\n", "\n", "autocorrelations = pd.DataFrame(\n", " index = pd.unique(reviews.author), \n", " columns = ['score', 'best_new_music']\n", ")\n", "\n", "for i, rows in reviews.groupby('author'):\n", " if rows.shape[0] < 5:\n", " autocorrelations = autocorrelations.drop(i)\n", " continue\n", " \n", " for j in autocorrelations.columns:\n", " autocorrelations.loc[i, j] = autocorrfun(rows, j)\n", "\n", "autocorrelations = autocorrelations.astype(float) \n", "autocorrelations.describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "score: p = 0.00237133392457\n", "best_new_music: p = 0.760200306934\n" ] }, { "data": { "image/png": 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CLMuQJAnPPvssRowYocHKiYjSz+HDh/Hggw9i8+bNKCwsxLXXXovrrrtO62URESWFLiq7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import ttest_1samp\n", "\n", "f, axs = plt.subplots(1,2)\n", "for i, col in enumerate(autocorrelations.columns.values):\n", " h = axs[i]\n", " data = autocorrelations[col].dropna()\n", " \n", " p = ttest_1samp(data,0).pvalue\n", " print(col + ': p = ' + str(p))\n", " sns.kdeplot(data, ax = h, shade=True,gridsize=200, legend = False)\n", " h.set_xlim(np.array([-1,1]))\n", " h.set_title(col)\n", " h.set_xlabel('Autocorrelation')\n", " h.set_ylabel('Density')\n", " \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In aggregate, there aren't systematically strong autocorrelations, especially in the base of best new music. Scores are slightly negatively autocorrelated, so high scoring reviews are likely to be followed by low scores, etc, but that difference isn't much to speak of.\n", "\n", "These estimates are probably obscured by the majority of middle-range scores though. Only 10% of reviews lie below a score of 5.5, and only 10% above 8.2 (see [this notebook](https://github.com/nolanbconaway/pitchfork-data/blob/master/notebooks/review-score-exploration.ipynb)). Authors probably don't feel they need to compensate for reviews within that range. Scores outside that range are comparatively unusual, and so if there were autocorrelations we'd see them there." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_next_reviews(df):\n", " res = pd.DataFrame(index = None, columns = reviews.columns)\n", " for a, rows in df.groupby('author'):\n", " nums = rows.review_num+1\n", " idx = (reviews.author == a) & reviews.review_num.isin(nums)\n", " res = res.append(reviews.loc[idx])\n", " return res\n", "\n", "\n", "score_split = dict(low = dict(), mid = dict(), high = dict())\n", "for i in score_split.keys():\n", " \n", " if i=='low':\n", " idx = reviews.score<5.5\n", " elif i == 'mid':\n", " idx = (reviews.score<=8.2) & (reviews.score>=5.5)\n", " elif i=='high':\n", " idx = reviews.score>8.2\n", " \n", " score_split[i]['N'] = reviews.loc[idx]\n", " score_split[i]['N+1'] = get_next_reviews(score_split[i]['N']) \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "high\t N mean= 8.70731434953\t N+1 mean=7.22078295912\n", "mid\t N mean= 7.12214522215\t N+1 mean=7.02043789872\n", "low\t N mean= 4.32439777641\t N+1 mean=6.7641509434\n" ] }, { "data": { "image/png": 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SaY6TT3OcXP1xfj+sPBJ/nWam09h46t7zJ2tbqGtowR4JM7bKKlto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\nJ8lVCF4CiMSS8U08G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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, k in enumerate(['high','mid','low']):\n", " data = score_split[k]['N+1']\n", " sns.kdeplot(data.score.dropna(), shade=True, label = k)\n", " \n", " m1 = score_split[k]['N+1'].score.mean()\n", " m0 = score_split[k]['N'].score.mean()\n", " \n", " print(k + '\\t N mean= ' + str(m0) + '\\t N+1 mean=' + str(m1))\n", " \n", "# plt.axis([0,10,0,1.6])\n", "\n", "\n", "plt.xlabel('Score')\n", "plt.ylabel('Density')\n", "plt.title('$N+1$ Distributions')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results\n", "\n", "Obviously there is a huge amount of overlap in the distributions following low, mid, and high scoring reviews. The marginally negative autocorrelations observed earlier are probably from a slight decline (0.1) in scores at times $N$ and $N+1$ for mid-range reviews. \n", "\n", "**But**, on average, albums reviewed after a low scoring review are scored nearly 0.5 _worse_ than those after a high scoring review. That's a surprisingly strong difference given how tightly scores are distributed in general. A score difference 0.5 absolutely makes a difference with respect to how the album is received: a 7.8 is almost modal, but an 8.3 is often _best new music_. \n", "\n", "In the [paper](https://academic.oup.com/qje/article/131/3/1181/2590011/) I referenced at the top of this page, a main interest was in how borderline decisions (i.e., the pitch could be a strike or a ball) are made based on previous decisions. Here I haven't paid special attention to these borderline cases, but it's likely these effects make an even bigger difference for albums that could or could not be considered best new music." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }