I developed this on Chrome on a Mac and Chromium on Ubuntu, and it worked on both of those. Sorry it's giving you problems.
I would've hazarded a guess at a Webkit bug, but OP mentioned she developed this on Chrome..
The author's work seems really useful for detecting spam. There are some people / bots who post a lot of specialist content. They only ever post links to content on domains that pay when visitors click links. These domains have a lot of ads. There's no other interaction on the site.
_NOT SAFE FOR WORK_:
This user (http://www.reddit.com/user/walfa2) only posts content from sites which pay when viewers see the images. The domains have heavy ad content, with popups etc.
Here's an example domain:
(http://www.reddit.com/domain/img1.picfoco.com/)
Once you find one user you can find a bunch of these domains, and the other users posting to those domains, and thus find a few more domains.
With a bit of tinkering you could should a colour coded chart of spam domains; of users that only post content from those domains; and users that never make replies but only make top level comments.
That could be run once a week and (with human oversight) used to remove content which is not good for reddit.
In the original title, 'Hidden' was 'Latent' - communities that de-facto exist even though they are not explicit. 'Latent' would have been a more precise word, but 'hidden' is more accessible.
If the posts are upvoted by the community, then it should be seen as a good and not a negative.
One of the oddities of reddit as compared to other social sites is that content owners and traffickers are looked-down upon simply because they can profit from attention.
I agree. To me it's not a problem. But unfortunately some of these posts leak into unsuitable subreddits. In a NSFW porn subredddit it's not much of a problem when someone links to a site heavy with horrible porn ads; but when that link is posted to a non-porn subreddit it's more of a problem.
And, really, Reddit is better as a community rather than a dump for links. So people who have no interaction with the site other than dumping links can be a problem. Being paid when people visit those links can make them more of a problem. They have no interest in Reddit.
The problem with this thinking is that vote fraud runs rampant and is sometimes nearly impossible to detect.
Edit: Here are the original threads, I don't think the project got very far. http://www.reddit.com/r/announcements/comments/ddz0s/reddit_...
http://www.reddit.com/r/redditdev/comments/dtg4j/want_to_hel...
EDIT: (Sorry for all the parentheticals.)
There are startups selling health data this way, I don't think it would be so bad for subreddit subscription data.
I just used post and comment histories, which suited my purposes fairly well because the larger project was looking into how memes spread.
http://www.reddit.com/r/redditdev/comments/dtg4j/want_to_hel...
Here's another version:
1. I'm assuming you downloaded comment threads from the front page of each the subreddits you looked at and then looked at the subreddit each of the posters had commented in. How many requests did you end up making?
2. Did you hand select the subreddits you analysed? If so, what criteria were you looking for?
3. Have you thought about doing any more research into this area? I made http://redditgraphs.com/ and was looking into ways of guessing a user's age & gender based on their commenting history. I found some papers about similar sites:
twitter: http://www.aclweb.org/anthology-new/D/D11/D11-1120.pdf
blogspot: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.136...
youtube: http://static.googleusercontent.com/external_content/untrust... (This one looks the most promising; using their methods, treat subreddits as youtube videos to create more accurate profiles of communities and users. They also examine the propagation of speech patterns which capture the spread of some memes.)
Unfortunately, reddit doesn't have user profiles or name-like user names (so there isn't an easily available training set) and I was having difficulties organizing and analyzing the large amount of data I was downloading, so I put the project aside. There has been basically no research done specific to reddit (http://scholar.google.com/scholar?as_ylo=2008&q=reddit+d...) which is surprising to me because of its size and unique subreddit system.
4. If you want to examine the spread of memes, you need access to old threads. http://stattit.com/ is the best way of getting around the reddit API's 1000 most recent post limitation.
5. Last month, a similar data set (which only looked at reddit) was collected - I think you're trying to do something different and your presention is much better, but you might be interested in the discussion: http://www.reddit.com/r/TheoryOfReddit/comments/126pth/scrap...
One thing I was wondering in terms of reddit research - have you looked into this at all - is that they have users check a specific box if they are ok with their voting data being used for research - even if it's already public. My question then is this - is it somehow wrong to use (already-public) data for research? Anyway, I talk about my original aims for the project in some other comments.
Thanks for the link to stattit. My strategy for getting enough threads for my other project was just to keep a slow scraper running for a month and then go back to it - stattit will be incredibly helpful.
Based on the dozens (at least) of papers published each year that use twitter data, I'm pretty sure it's kosher to use public posts. You might want to double check with your irb though. Depending on how you present the information, so users might be concerned about their privacy - I wrote a bot that replied to people posting variations of 'your comment history' with a link to the referenced person's redditgraph and several people said they were creeped out by it (a little more here, if your interested: http://www.roadtolarissa.com/redditgraphs-retrospective/).
Depending on what you are looking for the rate limit might slow you down a lot; you might want to contact the site admins:
> tl;dr If you need old data, we'd much rather work out a way to get you a data dump than to have you scrape.
https://groups.google.com/forum/?fromgroups=#!topic/reddit-d...
Such as what subreddits are /r/ liberals, conservatives, libertarians, anarchists, etc likely to follow?
Are liberals commonly in /r/trees? Are libertarians big on /r/economics? Are conservatives avoiding /r/wtf and /r/trees?
But there are a few things that kinda bother me with this:
The problem I can find with this data is that it isn't a representation of the reddit hidden communities as a whole, just the hidden communities of those who actually post (only 20% of Reddit).
A question I have is whether these are two-way connections with the groups. It's not clear exactly how the analysis is done 100% (perhaps I missed this portion), but could connections between subreddits be generated by there being a lot of people who post in a very tiny subreddit also posting in a larger subreddit? This means that though someone may like Large Subreddit A, they may not like the more specific Subreddit B. But a lot who like Subreddit B like Subreddit A.
There are always going to be the same people cross-subscribed between A and B as between B and A. This graph is not of the number of people cross-subscribed between two reddits - it's of the sum (number of people cross-subscribed)/(users in A) + (number of people cross-subscribed)/(users in B). So if a lot of people in a tiny subreddit are cross-subscribed, they get a big boost from the first term, but almost no boost if they make up a tiny sliver of subscribers to reddit B.
For instance, I usually read /r/bicycles, but also programming, motorcycles, cars, and 2xc. How many other people have that unique mix of interests?
