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Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics

  • Matthew C. Benigni
  • Kenneth Joseph
  • Kathleen M. Carley
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Social influence bot networks are used to effect discussions in social media. While traditional social network methods have been used in assessing social media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illustrated using data from three different activist communities on Twitter—the “alt-right,” ISIS sympathizers in the Syrian revolution, and activists of the Euromaidan movement. We observe a new kind of behavior that social influence bots engage in—repetitive @mentions of each other. This behavior is used to manipulate complex network metrics, artificially inflating the influence of particular users and specific agendas. We show that this bot behavior can affect network measures by as much as 60% for accounts that are promoted by these bots. This requires a new method to differentiate “promoted accounts” from actual influencers. We present this method. We also present a method to identify social influence bot “sub-communities.” We show how an array of sub-communities across our datasets are used to promote different agendas, from more traditional foci (e.g., influence marketing) to more nefarious goals (e.g., promoting particular political ideologies).

Keywords

Social media Social cyber-security Bots Social networks Network science Social influence Echo-chambers 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Matthew C. Benigni
    • 1
  • Kenneth Joseph
    • 2
  • Kathleen M. Carley
    • 1
  1. 1.Institute for Software Research, Carnegie Mellon UniversityPittsburghUSA
  2. 2.Computer Science and Engineering, SUNY BuffaloBuffaloUSA

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