What interests Reddit? A network analysis of 84M comments by 200K users

http://markallenthornton.com/blog/what-interests-reddit/

by alexcasalboni • 11 years ago

175 57 11 years ago

stared3• 11 years ago

I like a lot such analyses based on the network structure (not long ago I made something similar for Stack Exchange - http://stared.github.io/tagoverflow/; continuation of my older one https://github.com/stared/tag-graph-map-of-stackexchange/wik...).

Though, technology-wise, it is one use-case where SVG beats pixel graphics, both in terms of usability and interface (whether it is custom D3.js or something graph-oriented as http://sigmajs.org/).

wamatt• 11 years ago

if you set tag coloring to "% answered", an interesting pattern emerges

http://i.imgur.com/ZLmWHrq.png

responsiveness of the community in order of most to least

- oldschool hacker (c/c++,bash,perl,regex)

- web dev (jquery, javascript, html, css)

- app dev (ios, objectivec, android, java)

stared3• 11 years ago

^ edit: http://stared.github.io/tagoverflow/

Link "ate" the semicolon.

faizshah6• 11 years ago

I'm working on a project relevant to this. Does anyone know if the author has shared this data set anywhere? Or does anyone know of any data sets that could be used for developing mixture models to classify users into interest groups (like photographers, programmers etc)?

placeybordeaux• 11 years ago

Yeah the thing I most wanted out of the post is a torrent to the data.

faizshah6• 11 years ago

I have been working on a small shell script to get the top 500 posts of the top 5000 subreddits. The big issue is the api call limit is 60 req/min for OAuth2 tokens. It would save a lot of time if someone made a torrent of a data set like that, I will probably post the data I collect somewhere as well but it would be easier if reddit just hosted a data set like the one I described. They could probably post one on AWS public data sets. https://aws.amazon.com/datasets/

aroch• 11 years ago

If I'm remembering correctly, the raw data was given under an NDA/DND as a one time only deal. There was a subreddit associated with the data and collection but its since been banned.

minimaxir2• 11 years ago

I was the one with this data. The subreddit which discussed provided the data went private.

I have the raw data but it's infeasable to distribute due to size and the original source got in trouble for making it easily accessible.

Houshalter2• 11 years ago

The admins released a bunch of anonymize voting data once. And several people have distributed datasets of scraped comments. Sorry I don't have links handy. Check through /r/redditdev

faizshah6• 11 years ago

Thanks for the tip, I found the link you're referring to:

https://github.com/umbrae/reddit-top-2.5-million

stared3• 11 years ago

The author released data, links are at the bottom of the article (great thanks to him!).

faizshah6• 11 years ago

Thanks for the tip, I just started downloading the data.

fspacef• 11 years ago

Salute the effort put into this, quite thought provoking

okasaki• 11 years ago

Maybe I'm just stupid, but I don't see anything thought provoking.

In fact I feel that a better way to see what redditors are interested in would be to just find (there may even be stats on reddit on this) the ~50 most active subreddits.

zipppy• 11 years ago

If the 50 most active only constituted 10% of all reddit activity, though, they wouldn't necessarily paint an accurate picture of all of reddit.

Maybe nothing could paint that picture, but if there are themes prevalent independent of the subreddit topics themselves, this kind of analysis could shed light on them.

6stringmerc• 11 years ago

I get the feeling that Conde Nast may not like this type of approach when they're not directly profiting from it. A study of the language between the SFW and NSFW type tags might be pretty interesting, or, well, not very pleasant. I did participate in a couple music communities for a while, but there's something in the stew over there that I'm glad I closed my account and never looked back. YMMV.

brandonwamboldt3• 11 years ago

Contrary to popular belief, Condé Nast no longer owns Reddit.

Since 2012, Reddit operates as an independent company (Advanced Publications, the parent company of Condé Nast is a majority share holder though).

See: http://www.redditblog.com/2013/08/reddit-myth-busters_6.html...

thieving_magpie2• 11 years ago

So they don't own it, but they are the majority share holder? That doesn't feel very independent. Maybe I'm misunderstanding.

shillster• 11 years ago

Or even better, if we could systematically measure the brigades, moderator manipulation and psyops.

probably_wrong• 11 years ago

I've been thinking about that for a while in my free time. However, it's pretty much useless without actual data.

I wish there was a publicly available dataset, but given all the privacy implications, I doubt it.

grabcocque• 11 years ago

Misogyny seems to be big in Reddit comments.

seany• 11 years ago

You misspelled misandry.

sliverstorm• 11 years ago

I'm thinking misanthropy is more accurate

hooo• 11 years ago

I find these network visualizations nice to look at, but not all that insightful. They're generally hard to read and track relations outside of the main clusters. Am I missing something?

th0ma52• 11 years ago

No I don't think so. A lot of people call these things "hairballs," and probably a more useful interface would be some kind of faceted browser that allows you to do pivots and look at aggregate stats of the various lenses you can put on top of a graph. Additionally, measurements such as node separation, "betweenness," or perhaps even looking at common chain patterns are probably more useful ways of trying to dissect graph structures.

Chronic31• 11 years ago

Let's say you compute how distant/similar two concepts are. Then what? You update a link on Wikipedia?

Real_S• 11 years ago

You might enjoy mapequation.org

The methods used there are awesome! Also, open source with a great website.

SwellJoe• 11 years ago

What interests reddit? Casual racism and misogyny. Also cats.

Seriously though, it's interesting how interconnected some things can be in this view. I'm not sure what sense I can make of those interconnections, though. Mousing around, while being a very frequent redditor (so my own neural network is making connections based on experience), I can kinda infer order out of things like the "government->state" topics connected to "force" and "property" among others (hints at the libertarian-leaning general population), and the "women" topic connecting to a whole host of stuff...the cyan colored section off to the top right might even kinda hint at the casual misogyny thing (which was a "ha ha only serious" kind of joke), with words like "bullshit", "logic", "proof", "assumption", "reasoning", and "evidence", being connected to "women" but not to "men".

But, without having spent years on reddit, and without my particular flavor of reddit (the subs I'm subscribed to), maybe I'd interpret the data very differently. I never quite know how to interpret network graphs like this, honestly, short of for things that are networks. i.e. a computer network topology on a graph shows useful data...the hops from one machine to the next. When connecting up one word to the next, it seems difficult to draw meaningful conclusions. Like my interpretation of the meaning of "government->state->property" as being a hint at the libertarian leanings of many subreddits, or the connection of "women->reasoning->evidence" as being a hint of many redditors belief that women are illogical liars (which is the impression many of my female friends have of reddit, in general, particularly when topics like date rape or the "friend zone" come up). Is that actually the context in which these connections are made? I wouldn't really know how to check. It'd be cool to be able to drill down to conversations in which the connections where made, but presenting that in a coherent UI seems challenging.

thejaredhooper• 11 years ago

I agree. It would be nice to drill down into the data in order to further analyze everything. I also feel there was a particular sort of censorship in the dataset, an indicator of which was the explicit racist and misogynistic words that were absent. There was a large lack of swears and bad terms in this analysis (bitch being a particularly obvious cut) and I, for one, see examples of these slurs prevalently used by young men far too often on the site.

Perhaps the data was tailored when it was provided to the analyst, or it was censored after reception, but this felt too "PG-13" for an analysis of reddit's "interests".

jedberg2• 11 years ago

Fun fact: We did this exact analysis at reddit many years ago, and used it to figure out which subreddits were related to each other. We never got around to productizing it, unfortunately, but the idea was to use it to suggest new reddits to you.

sinemetu11• 11 years ago

I guess this might get into some special sauce territory, but was there a specific reason why this type of recommendation system was deprecated?

Houshalter2• 11 years ago

All the reddit subreddit recommenders I've seen produce garbage recommendations. Outside of a handful of popular, general subreddits which everyone already knows about, everything is niche special interest stuff that you need to find on your own.

jedberg2• 11 years ago

As others already said below, the feedback loop was dangerous.

Also it took a lot of resources to calculate and we just didn't have the time to build efficient map/reduce jobs to do it regularly. It was done by hand in Mathmatica.

swalsh• 11 years ago

One potential downside of using an algorithm like that is the possibility of a feedback loop.

eevilspock• 11 years ago

The very difficult trick is to find a virtuous feedback loop. Positive feedback loops might appear to be virtuous, but without balancing mechanisms they encourage viral dynamics (when did the virus become out role model for success?), group think and gaming.

Google's PageRank is an example of an incestuous positive feedback loop. Web pages with many inbound links get ranked higher, and pages that rank higher get more inbound links (because that's how many people find things they link to).

erroneousfunk• 11 years ago

Small point: Is it really considered "scraping" (" I scraped approximately 84 million comments") if you used a Python library that uses the Reddit API, not the actual site directly?

What interests Reddit? A network analysis of 84M comments by 200K users

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