Cheng, Justin, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. “Antisocial Behavior in Online Discussion Communities.” ICWSM. 2015.
Summary:
In this paper, Cheng et al present a study of users banned for antisocial behaviors sampled from three online communities (CNN, IGN and Breitbart). The author characterize antisocial behaviors by study specific groups from these online communities: FBUs(Future-Banned Users) and NBUs(Never-Banned Users). The author also presented analysis of “evolution over time” indicating FBUs write worse than other users over time and the community tolerance tend to decline over time. At last, the author proposed an approach to extract features for predicting antisocial behaviors, potentially automate and standardize this process.
Reflection:
I think there are several points noteworthy in this paper. First, these are three communities with different characteristics. Breitbart is far-right according to google, IGN don’t have a tendency and personally I consider CNN to be lean left. The nature of the community will attract certain user group and might result differently in user behaviors. Also the specific topics can lead to different results. But in the analysis I only see ‘measuring undesired behavior’. This is rather blurry description, but the term antisocial itself is hard to have a clear definition. This makes me curious because different communities have different banning rules, but carrying out these rules can vary accordingly. In this article it is simply categorized as banned and non-banned. Also the banning rule is different across different communities, but some data is treated as a single entity. To me this may not be completely solved due to the nature of the question, but can definitely be further elaborated or discussed. Also, the number of data samples are not consistent (18758 for CNN, 1164 for IGN, 1138 for Breitbart)
As for the proposed features and classifier to predict antisocial behaviors, I like the idea. While using bag of words can measure literal ‘trollings and abuse’. However a lot of antisocial behaviors online are one step further, which is not limited to literal words e.g. sarcasm. When sarcasm goes extreme, it can be antisocial. Identifying those specific antisocial behaviors can be easy within a interest group, but when there is an agreement in such groups, it is likely that this post get reported / deleted. Subjective Deleted / reported posts should not be the only metric for measuring antisocial behaviors. Objective features, such as using down votes, might reduce the influence of such subjective behavior from administrator. But needs further clarification. When you downvote in some communities, it provide options for you to choose the reason for such votes: disagree, irrelevant, or trolling. This will help the classifier get clarified response for down vote reasons.
Questions:
This paper study banned posts from 3 large communities, but different communities has different guidelines and what kind of guideline can be generalizable for all communities?
Is Antisocial Behavior / Language as main user banning criteria consistent for all cases discussed here? How can it be verified / pre-processed?
For CNN, I have the impression that user tend to view this website based on their political background. We also see a higher # posts reported% compared to websites like IGN which users are less ‘categorized’. Will the nature of the website have an influence on how users behave? (I mean for sure but this might be something noteworthy)
