Antisocial Behavior in Online Discussion Communities Authors: Justin Cheng, Cristian Danescu-Niculescu-Mizily, Jure Leskovec Presented by: Ananya Subburathinam

Introduction Characterizing antisocial behavior in online communities. Trolling, flaming, bullying, harassment, etc Purpose: Identify and understand antisocial behavior Important to community maintainers

Data Used CNN.com, Breitbart.com, IGN.com 18 months, 1.7 million users 40 million posts, 100 million votes Permanent ban lists Timestamped user activity

Characterizing Antisocial Behavior Compare Never-Banned users(NBU) and Future-Banned Users(FBU) FBU characteristics: Write less similarly Harder to understand Less positive language More profanity Posts more in individual threads

Typology of Antisocial users High deletion rate FBUs vs. Low Deletion Rate FBUs Higher deletion rate users: Write less similarly to others Post more in each discussion they are in Low deletion rate users: Posts spread across many discussions Attract less attention Who gets worse with time, and who gets better?

Predicting Future Banning Features used: Post content User activity Community response Moderators’ actions Reliable prediction from 5 – 10 posts Predict with over 80% AUC

Related Work What’s considered “Antisocial”? Trolling – “negatively marked online behavior”, flaming, griefing. Studies antisocial users and their evolution over time Large-scale data-driven analysis Goals – quantitative insights, early detection of trolls Other works: Detecting Wikipedia vandalism, bad behavior in online games.

Data Preparation Measuring Undesired Behavior Down votes, comments, reports, deletion or bans Deletions and bans are more precise Excludes temporary bans and link spammers

Data Preparation Matching FBUs and NBUs FBU tweets significantly more causing large difference Each FBU is matched with a similar NBU Measuring text quality Flaws with dictionary-based approach, classifiers trained on deleted posts. Human judgement – label 6000 random posts on a 1-5 scale Rating 3 or lower indicates an inappropriate post Train logistic regression model AUC of 0.70

Understanding Antisocial Behavior FBUs – write less less similarly to others have high ARI (less readable) Use less positive words, more swearing

How do FBUs generate activity around themselves? Community dependent Breitbart, IGN – more likely to respond to posts CNN – more likely to start discussions They get more responses than average users They post more per thread than average users

How do FBUs generate activity around themselves?

Evolution over time What happens to FBUs change over time? Degrading text quality Increases animosity from the community

Evolution Over Time First 10% and last 10% posts from 200 random NBUs and FBUs were rated from 1-5 by workers from Mechanical Turk on the basis of appropriateness.

Effect of time Comparison of posts of similar text quality, one from the first 10% of and one from last 10% of user’s life. Time had a significant effect on post deletion. Effect of censorship 2 groups : had 4 or more vs. one or less of first five posts deleted Match users from the two groups based on text quality of these posts Compare quality of subsequent posts Significant difference is observed Evolution Over Time

Types of Antisocial Users Users with differing deletion rates Bimodal – Hi-FBU and Lo-FBU Hi-FBU – get more responses, lower post quality, fewer threads, more posts per thread. Lo-FBU – lower post deletion rate, but rises in second half of their life. First half – more threads, second half – more posts in fewer threads

Antisocial Behavior in Two Phases Fit linear regression over the post deletion rates for the two halves of a user’s life The slopes of the two lines are m1 and m2.

Identifying Antisocial Users Factors: Post features Undesirable content, less readable Activity features more time in individual threads, posts per day, votes Community features Who interacts with a user Moderator features Posts deleted, slopes and intercepts of deletion rates,

Predicting antisocial behavior Predict from first ten posts with accuracy of 0.74 Classifier is robust, even without moderator features (AUC of 0.79) Only using proportion of deleted posts – AUC of 0.73 Baseline bag of words model – AUC of 0.69 (less across communities)

Predicting antisocial behavior

Possible Improvements suggested by authors More fine grained user labeling Analysis of user interactions in posts Effect of groups of users

Questions Is the term "antisocial behavior/user " social platform specific,if yes how would the approach of author scale to other social platforms? What characteristics/features must be taken into account to detect more d eceptive forms of antisocial behavior? Authors claim that "the more posts a user eventually makes, the more difficult it is to predict whether they will get eventually banned later on" is unclear from the paper? Why is it that the more users post, the more difficult it is to predict whether they will get banned later on? If a person had an emotional outburst on a particular topic and is classified as an FBU, the persons every post will be scrutinized more closely. Is that really necessary?

Questions How do you think authors could have added to the features in identifying anti­social behavior? How does the automatic tool for identification of antisocial users by t he first 10 posts work if the user posts positive comments initially but tends to go negative towards the end? As the analysis is limited to permanently banned users, whether this method can guarantee that innocent user is not going to banned or not if we use this classifier? If a user is posting antisocial post once in a while then how to identify that kind of user?