The growth of online discussion communities made them central to exchanging opinions, information, and knowledge, which gave those communities a significant role in forming opinions and knowledge for many of their users. With a little chance to verify both content and identity, a normal user could easily be prone to identity deception and misinformation. One of the challenges to a healthy online discussion community is sockpuppetry, where multiple “virtual” users are controlled by a single actual user. Sockpuppetry could be used for deception, or for creating a fake public consensus to manipulate the public opinion towards an event or a person.
In their work An Army of Me: Sockpuppets in Online Discussion Communities, Kumar et. al conducted a valuable study that aims to analyze the behavior of sockpuppets in online communities. Their study consists of a data-driven approach that analyzes data from nine different online discussion communities. Their study analyzes posting activity, and linguistic characteristics of content posted by sockpuppet accounts. After that, they identify different types of sockpuppets based on deceptive behavior and/or supportive behavior towards other sockpuppets. The study finally uses these analyses to build a predictive model that aims to predict whether an account is a sockpuppet and whether a pair of accounts is a sockpuppet pair.
My reflection on this study could be summarized in the following points:
- This study helps to build a clear definition of a sockpuppet and highlights some of the motivations behind creating such online identities. There could be a wide variety of motivations behind sockpuppetry, some could be benign, but it is highly important to understand the malicious motivations behind sockpuppetry.
- Activity traces of sockpuppets are highly predictive, while community reactions towards them are less predictive. Compared to other types of antisocial behavior, community reactions features were more predictive as shown by Cheng et. al in Antisocial Behavior in Online Discussion Communities. This fact could convey multiple signals. It could be that sockpuppets are hard to detect by their surrounding community, or that a user in an online community is more alert by antisocial content rather than a specific activity pattern. Which may mean that a community tends to react negatively if a sockpuppet account is posting a significantly undesirable content, more than that if a strange activity pattern occurs unless it is blatantly suspicious.
- Sockpuppets seem to tend to work in a group. Although the study shows that most of the identified sockpuppets tend to work in pairs, there could be other activity patterns associated with larger groups of sockpuppet accounts that are worth studying.
- Similar to other data mining problems on online communities, it seems to still be a hard problem to develop an automated system that could reliably replace human moderators, however, reaching a step where an automatic flag could be raised on some content makes life easier for moderators and help towards a faster control of the spread of any type of undesirable behavior in online communities.
This study stimulated my interest to proceed in different directions. I would be interested to study the role of sockpuppets in different historical events such as the Arab spring or the US 2016 elections. I’d also be interested to study how sockpuppets are effective in forming opinions of normal users, or in spreading misinformation and making it widely accepted by users of online communities.
There are also multiple directions to improve the current study. Among them is the study of the activity pattern of larger sockpuppet groups, a deeper analysis of the content posted by sockpuppets beside their activity patterns to derive more content-based features, and a further analysis and comparison of the activity patterns and content features of sockpuppets across different types of online communities.
