Overview
This paper is about a study conducted to characterize antisocial behavior in large online discussion communities. The study aims to answer three questions: are there users that only become antisocial later in their community life, does a community’s reaction to antisocial behavior improve the behavior, and can antisocial users be identified before they cause any issues in the community? The study used CNN.com, Breitbart.com, and IGN.com to conduct their analysis of online discussion communities. They found that users who were eventually banned from these communities concentrated their efforts on a smaller number of threads, wrote worse than other users over time, and were exacerbated by other users over time. Using these conclusions the author created a classifier to try to identify negative users before the antisocial behavior got them banned by the websites moderators.
Reflection
- The websites chosen by the author for use in the study are known to house biased and controversial discussions. The banning of users or antisocial behavior on these websites may be inherent to the nature of the topics being discussed. This is specifically true on CNN.com and Breitbart.com. Given the polarizing nature of politics, these websites are not so much discussion communities and may be more susceptible to users engaging in undesired behaviors more so than other discussion communities that are actually communities. Websites such as Breitbart.com have content that is inflammatory to some and may lead to outbursts that this study would call antisocial.
- The paper also states that users that violate the community norms are eventually permanently banned from the discussion community. The author uses this as their “ground truth” for analyzing antisocial behavior online. However, communities such as Breitbart.com and CNN.com likely contain many users with similar views and biases. A user that violates these norms and is banned may not necessarily be displaying antisocial behavior. It is also possible that the banned user disagreed with the views of the community or did not hold their same beliefs. These differences between users and the specific community they engaged with could have led to them violating that communities norms and being banned. That does not mean their behavior was antisocial, rather it could have just been following a different set of norms than that found in the subset of society on Breitbart.com or CNN.com. I think an interesting question would be what portion of users banned from websites like Breitbart.com and CNN.com help viewpoints opposing the majority of users on those platforms?
- The paper employs the use of human workers to label the appropriateness of a post. Doing this introduces bias into the ratings inherent with the introduction of humans. The three websites chosen represent three widely different communities, that are each different in some way from the mainstream culture. Asking workers to rate these posts who have no experience with the community they are rating or what is appropriate for that setting has the potential to produce inaccurate or biased ratings as each of the workers has to rely on their own opinions and beliefs to decide what is appropriate.
- Later in the paper they use the fact that those who are banned from websites tend to write posts that are less similar than the others on the forum. I do not think this is a clear indication the posts included antisocial behavior. If the users are very different from the community they are communicating with that may be the reason they are banned. It could be a difference of opinion or personality, not necessarily antisocial behavior.
- The author also uses the rate of post deletion to predict banning and characterize the user as antisocial. However, a high rate of post deletion could predict banning as it shows that the moderator, who eventually bans the user, does not like having that user’s content on their forum. Using this as a predictor could be gauging the moderator’s opinion of the user more than any antisocial behavior the user is displaying. Furthermore, user banning, as a metric of antisocial behavior seems like it could miss many cases. Being banned from an online community, especially those that act as echo chambers for a similar set of opinions, does not necessarily mean that the behavior was antisocial. How can we measure antisocial behavior without including differences between users and the community?
I think this paper addresses an important issue as online discussion and discourse becomes more prevalent. I found some issue with how the study was conducted as it seems they are measuring how different some users are from the overall community and how the moderator feels about them more so than their antisocial behavior. By using the deletion of posts and difference from other posts as the main measures of antisocial behavior, they are really measuring the moderator’s view of the user and how much they stick out from the overall community. The article states that if becomes harder for their classifier to predict if a user will be banned the further in time it is from when they are actually banned. This helps illustrate that they are measuring the moderator opinion of the user rather than the underlying antisocial behavior as it makes sense that a moderator will delete more of the users posts (their main feature in classification) soon before they ban the user. For future research, analysis of the content of posts by users who display antisocial behavior over the life of their account would be interesting to see how this behavior begins and how community features interact with this behavior.
