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Measuring Social Spam and the Effect of Bots on Information Diffusion in Social Media

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Complex Spreading Phenomena in Social Systems

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Bots have been playing a crucial role in online platform ecosystems, as efficient and automatic tools to generate content and diffuse information to the social media human population. In this chapter, we will discuss the role of social bots in content spreading dynamics in social media. In particular, we will first investigate some differences between diffusion dynamics of content generated by bots, as opposed to humans, in the context of political communication, then study the characteristics of bots behind the diffusion dynamics of social media spam campaigns.

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Notes

  1. 1.

    RoboLike: https://robolike.com/.

  2. 2.

    Pandora bot: https://developer.pandorabots.com/.

  3. 3.

    http://truthy.indiana.edu/botornot.

  4. 4.

    Twitter Search API: https://dev.twitter.com/rest/public/search.

  5. 5.

    Twitter Stream API: https://dev.twitter.com/streaming/overview.

  6. 6.

    Bot or Not Python API: https://github.com/truthy/botornot-python.

  7. 7.

    Bot or Not Website: https://truthy.indiana.edu/botornot/.

  8. 8.

    The combination of the top 250 non-spam keywords, plus the 87 spam keywords, accounts for over 90% of all tweets in the original dataset.

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Authors and Affiliations

  1. University of Southern California, Information Sciences Institute, Los Angeles, CA, USA

    Emilio Ferrara

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  1. Emilio Ferrara
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Correspondence to Emilio Ferrara .

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  1. Technical University of Denmark, Lyngby, Denmark

    Sune Lehmann

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Ferrara, E. (2018). Measuring Social Spam and the Effect of Bots on Information Diffusion in Social Media. In: Lehmann, S., Ahn, YY. (eds) Complex Spreading Phenomena in Social Systems. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_13

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