WSV: Wall Street Vets

Scraper to see if /r/wallstreetbets was actually talking about $SLV and dumping $GME in early February, as reported by many media outlets.

I'm not formally affiliated with /r/wallstreetbets, nor am I informally part of that community.

How it works

scrape.py: Scrape a bunch of recent posts and comments from /r/wsb and save them as JSON locally.

Reddit's API makes getting posts/comments in a date range non-trivial; ideally, we could use PushShift, but that's been down for some time (probably because a bunch of people are trying to do the same thing that I'm trying to do). So, the next best thing is to get as many posts as possible (by crawling new/rising/controversial/top/etc) and hoping that the sample is large and representative enough.
load.py: Load JSON into a SQLite database.

This is a tradeoff; the time spent fetching posts and comments over the network is more expensive than disk space. It would be simpler to load the crawled data directly into an SQLite database, but I wasn't done with that at the time I wanted to start crawling.
plot.py: Query the SQLite database to generate plots using plotly.

Usage

Dependencies

This project was developed using Python 3.9.1.

$ pip install -r requirements.txt

Authenticating to Reddit

To authenticate to Reddit, you need to:

Create an app using your Reddit account.
Run vendor/refresh-token.py. See the praw documentation for more information.

Save your credentials in praw.ini:

[DEFAULT]
client_id=...
client_secret=...
refresh_token=...

Collect and analyze data

$ python scripts/scrape.py wallstreetbets fetch-posts
$ python scripts/scrape.py wallstreetbets fetch-comments
$ python scripts/load.py wallstreetbets  # creates wallstreetbets.db
$ python plot.py wallstreetbets.db all plot

Lessons learned

The initial crawl took about two weeks due to the sheer amount of comments and Reddit's API rate limiting. The first time around, I neglected to save scores for each comment. This second pass took more than four weeks; in the first pass, I was able to retrieve thousands of comments with a single API call, but during the second pass, I retrieved each comment one by one, which was more expensive. By the time I realized this, I was too busy to fix the problem, and let the scraper continue running as written.

I could have avoided this problem by being greedier about what data I was saving in the first pass. (In my defense, I was rtunning out of disk space on my laptop, but I think I would have made the same mistake even if I wasn't.)