Paper: Antisocial Behavior in Online Discussion Communities
Summary:
This paper talks about characterizing anti-social behavior which includes trolling, flaming, and griefing, in online communities. For this study the authors have focussed on CCN.com, Breitbart.com and IGN.com. The authors have presented a retrospective longitudinal analysis to quantify anti-social behaviour throughout an individual user’s tenure in a community. They have divided users in two groups – Future Banned Users(FBUs) and Never Banned Users(NBUs) based on the language and the frequency of their posts.
Reflection:
The paper ‘Antisocial Behavior in Online Discussion Communities’ focuses on detecting the anti-social users at an early stage by evaluating their posts. Their results are based on the features – post content, user activity, community response, and the actions of community moderators. In my opinion “What leads an ordinary man to exhibit trolling behaviour” should also have been considered as a contributing feature.
For example, in communities or forums where political discussions are held, comments exhibiting strong opinions are bound to be seen. I therefore feel that “What is considered anti-social depends on the particular community and the topic around which the respective community is formed” [1].
What struck my mind was there can be scenarios where discussion context determines the trolling behaviour of an individual. However, the ‘Readability Index’ parameters which authors have considered looked promising.
In the Data Preparation stage to measure “Undesired Behaviour” the authors have stated that “At the user-level, bans are similarly strong indicators of antisocial behaviour”. How is a user getting banned from an online community determines antisocial behaviour? For example, a user got banned from Stack overflow because all of the questions posted were out of scope.
The paper majorly revolves around the 2 hypothesis which authors have stated to evaluate an increase in the post deletion rate. H1: a decrease in posting quality, H2: an increase in community bias. To test both H1 and H2, the authors have conducted 2 studies – 1. Do writers write worse over time? This study is somewhat agreeable where one can analyse how the user writing is changing over time. 2. Does community tolerance change over time? According to the results presented by the authors this indeed looks true. However, in my opinion It also depends on how opinions/comments are perceived by other members of the community.
In the closing note, the paper presents some interesting facts about how to identify trolls and ban them at the very early stages.
[1] https://www.dailydot.com/debug/algorithm-finds-internet-trolls/
