3 ms·

The work is here: http://arxiv.org/pdf/1504.00680v1.pdf http://arxiv.org/pdf/1504.00680v1.pdf According to the data, FBUs (future banned users, aka trolls) are

The work is here: http://arxiv.org/pdf/1504.00680v1.pdf http://arxiv.org/pdf/1504.00680v1.pdf According to the data, FBUs (future banned users, aka trolls) are significantly more readable in their writing than NBUs (never banned users). That's certainly surprising. Also of note is that FBUs tend to reply more than NBUs, which actually meshes with some features of HN -- the inability to reply to a nested comment from within the nest (one has to click on the comment itself to reply -- though this feature has always been somewhat ambiguous and not really deterrent of impulsive replies; I don't even notice it anymore). Also, I think HN deflates the score of posts with many comments, a strategy supported by this line in the paper: "Prior work also identified post frequency as a signal of a low quality discussion (Diakopoulos and Naaman 2011)." An absolutely fascinating paper. What are the consequences, from an IP standpoint, of taking this paper and implementing features based on its conclusions?

existencebox 12y ago
Wish I had something more constructive to add than this hand waving, but perhaps an interpretation (via metaphor) of the first point: If you fight with a weapon, don't you become skilled with it?
michaelkeenan 12y ago
It's a great paper, and a great application of machine learning! Though, the FBUs had worse readability than NBUs. I think you're misinterpreting the readability index - higher is worse, not better. From the paper: "Users who get banned in the future (FBUs) (a) write less similarly to other users in the same thread, (b) write posts that are harder to read (i.e., have a higher readability index), and (c) express less positive emotion."
- diminoten 12y ago
  I saw that later on, I was specifically referencing chart 1(b).
bduerst 12y ago
Depends on the implementation. As a pre-banning feature, this mechanism from the paper may be a little heavy handed. Though the mechanism can be used to sift comment content, and highlight/raise the best content to the top based on user behavior. I'm surprised someone hasn't applied ML to reddit comments, in an attempt to predict comment quality. They certainly have the content for it.

existencebox 12y ago

Wish I had something more constructive to add than this hand waving, but perhaps an interpretation (via metaphor) of the first point: If you fight with a weapon, don't you become skilled with it?

michaelkeenan 12y ago

It's a great paper, and a great application of machine learning! Though, the FBUs had worse readability than NBUs. I think you're misinterpreting the readability index - higher is worse, not better. From the paper: "Users who get banned in the future (FBUs) (a) write less similarly to other users in the same thread, (b) write posts that are harder to read (i.e., have a higher readability index), and (c) express less positive emotion."

diminoten 12y ago
I saw that later on, I was specifically referencing chart 1(b).

bduerst 12y ago

Depends on the implementation. As a pre-banning feature, this mechanism from the paper may be a little heavy handed. Though the mechanism can be used to sift comment content, and highlight/raise the best content to the top based on user behavior. I'm surprised someone hasn't applied ML to reddit comments, in an attempt to predict comment quality. They certainly have the content for it.