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Utilities for Reddit Data Science

The reddit_cdsc project contains tools for working with Reddit data. The project is designed for the hyak super computing system at The University of Washington. It consists of a set of python and bash scripts and uses the Pyspark and pyarrow to process large datasets. As of November 1st 2020, the project is under active development by Nate TeBlunthuis and provides scripts for:

• Pulling and updating dumps from Pushshift in pull_pushshift_comments.sh and pull_pushshift_submissions.sh.
• Uncompressing and parsing the dumps into Parquet datasets.
• Running text analysis based on TF-IDF including
  • Extracting terms from Reddit comments in tf_comments.py
  • Detecting common phrases based on Pointwise mutual information "Wikipedia article on pointwise mutual information")
  • Building TF-IDF vectors for each subreddit idf_comments.py and (more experimentally) at the subreddit-week level idf_comments_weekly.py
  • Computing cosine similarities between subreddits based on TF-IDF term_cosine_similarity.py.

Right now, two steps are still in earlier stages of progress:

• Approach comparable to tf-idf for similarity between subreddits in terms of comment authors.
• Clustering subreddits based on cosine-similarities using power iteration clustering (PIC)

The TF-IDF for comments still has some kinks to iron out to remove hyper links and bot comments. Right now subreddits that have similar automoderation messages appear very similar.

The user interfaces for most of the scripts are pretty crappy and need to be refined for re-use by others.

Pulling data from Pushshift

• pull_pushshift_comments.sh uses wget to download comment dumps to /gscratch/comdata/raw_data/reddit_dumps/comments. It doesn't download files that already exists and runs check_comments_shas.sh to verify the files downloaded correctly.

• pull_pushshift_submissions.sh does the same for submissions and puts them in /gscratch/comdata/raw_data/reddit_dumps/comments.

Building Parquet Datasets

Pushshift dumps are huge compressed json files with a lot of metadata that we may not need. It isn't indexed so it's expensive to pull data from just a handful of subreddits. It also turns out that it's a pain to read these compressed files straight into spark. Extracting useful variables from the dumps and building parquet datasets will make them easier to work with. This happens in two steps:

1. Extracting json into (temporary, unpartitioned) parquet files using pyarrow.
2. Repartitioning and sorting the data using pyspark.

The final datasets are in /gscratch/comdata/output.

• reddit_comments_by_author.parquet has comments partitioned and sorted by username (lowercase).
• reddit_comments_by_subreddit.parquet has comments partitioned and sorted by subreddit name (lowercase).
• reddit_submissions_by_author.parquet has submissions partitioned and sorted by username (lowercase).
• reddit_submissions_by_subreddit.parquet has submissions partitioned and sorted by subreddit name (lowercase).

Breaking this down into two steps is useful because it allows us to decompress and parse the dumps in the backfill queue and then sort them in spark. Partitioning the data makes it possible to efficiently read data for specific subreddits or authors. Sorting it means that you can efficiently compute agreggations at the subreddit or user level. More documentation on using these files is available here.

TF-IDF Subreddit Similarity

TF-IDF is common and simple information retrieval technique that we can use to quantify the topic of a subreddit. The goal of TF-IDF is to build a vector for each subreddit that scores every term (or phrase) according to how characteristic it is of the overall lexicon used in that subreddit. For example, the most characteristic terms in the subreddit /r/christianity in the current version of the TF-IDF model are:

Term	tf_idf
christians	0.581
christianity	0.569
kjv	0.568
bible	0.557
scripture	0.55

TF-IDF stands for "term frequency - inverse document frequency" because it is the product of two terms "term frequency" and "inverse document frequency." Term frequency quantifies the amount that a term appears in a subreddit (document). Inverse document frequency quantifies how much that term appears in other subreddits (documents). As you can see on the Wikipedia page, there are many possible ways of constructing and combining these terms.

x + y = z_{1,d}

I chose to normalize term frequency by the maximum (raw) term frequency for each subreddit: \mathrm{tf}_{t,d} = \frac{f_{t,d}}{\sum_{t^{'} \in d}{f_{t^{'},d}}}

I use the log inverse document frequency: \mathrm{idf}_{t} = log\frac{N}{| {d \in D : t \in d} |}

I then combine them using some smoothing to get:

\mathrm{tfidf}_{t,d} = (0.5 + 0.5 \cdot \mathrm{tf}_{t,d}) \cdot \mathrm{idf}_{t}