Cats play DotA2. Dogs play Football
Deriving meaning from the comments of internet strangers
For our project, we accessed every publicly available Reddit comment for the month of August 2015
via a data dump shared on reddit.
The dataset is very large, composing of over 58 million comments, each represented as a JSON object,
and is 28 GB uncompressed. We looked at the top 100 words (excluding stopwords) for each of our
selected subreddits and collected count and score data for each word/subreddit combination.
Due to words appearing in the top 100 of multiple subreddits, we ended up with exactly 304 words
in our final dataset. Additionally, since certain subreddits had a much larger userbase,
we normalized the count and score statistics to allow for a fair comparison across subreddit groupings.
28
gigabytes of data
58,075,327
comments
55,303
subreddits
Our visualization provides the user with a means to visually explore differences (and similarities)
in language across different Reddit communities (subreddits). Specifically, we want to explore
differences in communities which are typically considered “opposites,” such as cats and dogs.
We pulled word usage data from Reddit’s publicly available comment database for eight popular
subreddits, or four pairs of “opposed” subreddits.
AskMen
(143,525 comments)AskWomen
(136,741 comments)cats
(21,127 comments)dogs
(27,304 comments)nfl
(563,733 comments)soccer
(497,445 comments)DotA2
(618,781 comments)leagueoflegends
(1,147,414 comments)
Our first visualization shows side-by-side wordclouds for each group of opposed subreddits,
with the size of the word relative to its normalized count in that particular subreddit.
Use the buttons at the top to choose subreddits for comparison. Hover over each word to see its position in the opposing wordcloud, as well as summary statistics in the center box.
Our second visualization compares two groups of opposing subreddits, four subreddits total.
The words appear as circles, positioned on an xy axis based on their normalized comment scores.
The circles are colored according to their total score, so red dots had a high average score
while blue dots had a lower average score. Use the buttons at the bottom to select subreddits
for comparison. Hover over the circles to view words, and click to view the normalized score
in each subreddit.
Top/Bottom:
Left/Right:
This project was completed as part of the Data Visualization and Communication course in UC Berkeley's
Master of Information and Data Science
program. We enjoy cats, dogs, and long walks on the beach. You can check out how we created this visualization on our
github repo. Thanks for viewing!
Questions/Comments/Requests?
Shelly
Charlie
Bovard
