# An Army of Me: Sockpuppets in Online Discussion Communities

arXiv: https://arxiv.org/abs/1703.07355 · HTML: https://mlsanity.com/paper/1703.07355/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2017.

## Built on by (most cited first, 18 of 18)

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- [To Tell The Truth: Language of Deception and Language Models](https://mlsanity.com/paper/2311.07092/) (2023)
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- [Characterizing Knowledge Manipulation in a Russian Wikipedia Fork](https://mlsanity.com/paper/2504.10663/) (2025)
- [Detecting Sockpuppetry on Wikipedia Using Meta-Learning](https://mlsanity.com/paper/2506.10314/) (2025)
- [Improving Authorship Verification using Linguistic Divergence](https://mlsanity.com/paper/2103.07052/) (2021)
- [Evaluating Deception Detection Model Robustness To Linguistic Variation](https://mlsanity.com/paper/2104.11729/) (2021)
- [Co-Membership-based Generic Anomalous Communities Detection](https://mlsanity.com/paper/2203.16246/) (2022)
- [User Identity Linkage in Social Media Using Linguistic and Social Interaction Features](https://mlsanity.com/paper/2308.11684/) (2023)
- [ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model](https://mlsanity.com/paper/2507.15067/) (2025)
- [AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths](https://mlsanity.com/paper/2609.06771/) (2026)

## Abstract (arXiv)

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 tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.