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

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Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining

Part of the book series: Lecture Notes in Social Networks ((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).

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Notes

  1. 1.

    Social bot networks are also referred to as Sybils in the computer security literature [20, 21].

  2. 2.

    And, although we have not discussed it here, likely content as well.

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Author information

Authors and Affiliations

  1. Institute for Software Research, Carnegie Mellon University, Pittsburgh, PA, USA

    Matthew C. Benigni & Kathleen M. Carley

  2. Computer Science and Engineering, SUNY Buffalo, Buffalo, NY, USA

    Kenneth Joseph

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  1. Matthew C. Benigni
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  2. Kenneth Joseph
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  3. Kathleen M. Carley
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Correspondence to Kathleen M. Carley .

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Editors and Affiliations

  1. Collaboratorium for Social Media and Online Behavioral Studies (COSMOS) Information Science Department, University of Arkansas at Little Rock, Little Rock, AR, USA

    Nitin Agarwal

  2. Intellectera AB, Stockholm, Sweden

    Nima Dokoohaki

  3. Collaboratorium for Social Media and Online Behavioral Studies (COSMOS), University of Arkansas at Little Rock, Little Rock, AR, USA

    Serpil Tokdemir

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Benigni, M.C., Joseph, K., Carley, K.M. (2019). Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics. In: Agarwal, N., Dokoohaki, N., Tokdemir, S. (eds) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-94105-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-94105-9_2

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