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Multidisciplinary International Symposium on Disinformation in Open Online Media

MISDOOM 2019: Disinformation in Open Online Media pp 77–88Cite as

Detecting Malicious Social Bots: Story of a Never-Ending Clash

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12021))

Abstract

Recently, studies on the characterization and detection of social bots were published at an impressive rate. By looking back at over ten years of research and experimentation on social bots detection, in this paper we aim at understanding past, present, and future research trends in this crucial field. In doing so, we discuss about one of the nastiest features of social bots – that is, their evolutionary nature. Then, we highlight the switch from supervised bot detection techniques – focusing on feature engineering and on the analysis of one account at a time – to unsupervised ones, where the focus is on proposing new detection algorithms and on the analysis of groups of accounts that behave in a coordinated and synchronized fashion. These unsupervised, group-analyses techniques currently represent the state-of-the-art in social bot detection. Going forward, we analyze the latest research trend in social bot detection in order to highlight a promising new development of this crucial field.

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Notes

  1. 1.

    http://reports.weforum.org/global-risks-2017.

  2. 2.

    https://about.twitter.com/en_us/values/elections-integrity.html.

  3. 3.

    Source: https://www.dimensions.ai/.

  4. 4.

    https://newsroom.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/.

  5. 5.

    https://help.twitter.com/en/rules-and-policies/platform-manipulation.

  6. 6.

    https://botometer.iuni.iu.edu/bot-repository/datasets.html.

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Acknowledgments

This research is supported in part by the EU H2020 Program under the scheme INFRAIA-1-2014-2015: Research Infrastructures grant agreement #654024 SoBigData: Social Mining & Big Data Ecosystem.

Author information

Authors and Affiliations

  1. Institute for Informatics and Telematics (IIT-CNR), Pisa, Italy

    Stefano Cresci

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  1. Stefano Cresci
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Correspondence to Stefano Cresci .

Editor information

Editors and Affiliations

  1. Institut für Wirtschaftsinformatik, WWU Münster, Münster, Nordrhein-Westfalen, Germany

    Christian Grimme

  2. LIACS, Leiden University, Leiden, Zuid-Holland, The Netherlands

    Mike Preuss

  3. LIACS, Leiden University, Leiden, Zuid-Holland, The Netherlands

    Frank W. Takes

  4. Department of Communication, University of Münster, Münster, Germany

    Annie Waldherr

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Cresci, S. (2020). Detecting Malicious Social Bots: Story of a Never-Ending Clash. In: Grimme, C., Preuss, M., Takes, F., Waldherr, A. (eds) Disinformation in Open Online Media. MISDOOM 2019. Lecture Notes in Computer Science(), vol 12021. Springer, Cham. https://doi.org/10.1007/978-3-030-39627-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-39627-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39626-8

  • Online ISBN: 978-3-030-39627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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