Assessing temporal and spatial features in detecting disruptive users on Reddit

Abstract Trolling,echochambersandgeneralsuspiciousbehaviouron- line are a serious cause of concern due to their potential disruptive effects beyond social media. This motivates a better understanding of the char- acteristics of disruptive behaviour on the internet and methods of detec- tion. In this work, we focus on Reddit which provides a rich social media platform for community-focused interactions. Using network representa- tions 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, relative to the wider population. We explore how these signals contribute to the accurate prediction of disrup- tive 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.
  • James Ashford (Cardiff)
  • Liam Turner (Cardiff)
  • Roger Whitaker (Cardiff)
  • Alun Preece (Cardiff)
  • Diane Felmlee (PSU)
Date Sep-2020
Venue 4th Annual Fall Meeting of the DAIS ITA, 2020