Assessing temporal and spatial features in detecting disruptive users on Reddit

James R. Ashford, Liam D. Turner, Roger M. Whitaker, Alun Preece, Diane Felmlee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Trolling, echo chambers and general suspicious behaviour online are a serious cause of concern due to their potential disruptive effects beyond social media. This motivates a better understanding of the characteristics of disruptive behaviour on the internet and methods of detection. In this work we focus on Reddit which provides a rich social media platform for community focused interactions. Using network representations of user activity alongside temporal statistics and other features we assess the behaviour of a sample of potentially disruptive users, based on their assigned comment karma (an aggregate of a user's comment up-votes), relative to the wider population. We explore how these signals contribute to the accurate prediction of disruptive users, and note that this is achieved without requiring any semantic analysis. Our results show that it is possible to detect signs of disruptive behaviour with good accuracy using limited inputs that are primarily based on the reply patterns that users generate. This is of potential value for large-scale detection problems and operation across different languages.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages892-896
Number of pages5
ISBN (Electronic)9781728110561
DOIs
StatePublished - Dec 7 2020
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/TerritoryNetherlands
CityVirtual, Online
Period12/7/2012/10/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Social Psychology
  • Communication

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