An Army of Me: Sockpuppets in Online Discussion Communities

@article{Kumar2017AnAO,
  title={An Army of Me: Sockpuppets in Online Discussion Communities},
  author={Srijan Kumar and Justin Cheng and Jure Leskovec and V. S. Subrahmanian},
  journal={Proceedings of the 26th International Conference on World Wide Web},
  year={2017}
}
In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets… 

Analyzing user discussion dynamics in social media platforms

This work presents a novel quantification of conflict in an online discussion, and studies how the sentiment of a user towards entities can be predicted using the tweets the user has posted so far, to explore user behavior and the underlying conversation patterns.

Bots increase exposure to negative and inflammatory content in online social systems

Analysis of large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, identifies the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots.

Community Interaction and Conflict on the Web

This work study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community, and finds that conflicts are marked by formation of echo chambers.

User Engagement with Digital Deception

This chapter presents the key findings of the recent studies in this area to explore user engagement with trustworthy information, misinformation, and disinformation framed around three key research questions: Who engages with mis- and dis-information, how quickly does the audience engage, and what feedback do users provide.

Antisocial Behavior on the Web: Characterization and Detection

This tutorial presents the state-of-the-art research spanning two aspects of antisocial behavior: characterization of their behavioral properties, and development of algorithms for identifying and predicting them.

User awareness and defenses against sockpuppet friend invitations in Facebook

Friend relations in Facebook have been used to access private and sensitive user data, post false or abusive information on the timelines and news feeds of victims, and scam and influence the

User Identity Linkage in Social Media Using Linguistic and Social Interaction Features

This work proposes a machine learning-based detection model, which uses multiple attributes of users’ online activity in order to identify whether two or more virtual identities belong to the same real natural person.

Characterizing, Detecting, and Predicting Online Ban Evasion

It is found that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes — from similarity in usernames and edited pages to similarity in content added to the platform and its psycholinguistic attributes.

A Time-Series Sockpuppet Detection Method for Dynamic Social Relationships

A weight representation method is designed to record the dynamic growth of sockpuppet’s social relationships and then transferred to a similarity time-series analysis problem, which obtains excellent detection performance, significantly outperforming previous methods.
...

References

SHOWING 1-10 OF 50 REFERENCES

Antisocial Behavior in Online Discussion Communities

This paper characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities, finding that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users.

A sock puppet detection algorithm on virtual spaces

Sockpuppet Detection in Online Discussion Forums

This paper proposes two approaches to identify sock puppets that occur in the same forum as well as cross forum and shows that the methods are effective.

Linking Accounts across Social Networks: the Case of StackOverflow, Github and Twitter

It is shown how tens of thousands of accounts in StackOverflow, Github, and Twitter could be successfully linked, and a comparative study of user interaction networks in the three platforms is conducted, and correlations between users interactions across the different networks are investigated.

Branded with a scarlet "C": cheaters in a gaming social network

It is observed that the cheating behavior appears to spread through a social mechanism: the presence and the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future.

Oxford Handbook of Internet Psychology

Over one billion people use the Internet globally. Psychologists are beginning to understand what people do online, and the impact being online has on behaviour. It's making us re-think many of our

Timeprints for identifying social media users with multiple aliases

This article shows how an author’s identity can be unmasked in a similar way using various time features using a timeprint, which can be seen as a type of fingerprint when identifying users on social media.

Detection of Multiple Identity Manipulation in Collaborative Projects

This article proposes a set of features that grows on previous literature to use in automatic data analysis in order to detect the Sockpuppets accounts created on EnWiki and compares several machine learning algorithms to show that the new features and training data enable to detect 99\% of fake accounts, improving previous results from the literature.

On the Feasibility of Internet-Scale Author Identification

In over 20% of cases, the classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of Cases the correct author is one of the top 20 guesses.

An analysis of social network-based Sybil defenses

It is demonstrated that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order to make their attacks more effective.