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Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter

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Security and Privacy in Social Networks and Big Data (SocialSec 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14097))

Abstract

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called “troll” accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter. We construct and analyze a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques and a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts, and we find that one troll account appears in the top-100 and four in the top-1000. This, combined with other findings presented in this paper, constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that, on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000, respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.

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Notes

  1. 1.

    https://blog.mozilla.org/internetcitizen/2018/01/19/10-twitter-bots-actually-make-internet-better-place/.

  2. 2.

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

  3. 3.

    https://doi.org/10.5281/zenodo.6526783.

  4. 4.

    https://about.twitter.com/en/our-priorities/civic-integrity.

  5. 5.

    https://www.tweepy.org/.

  6. 6.

    https://developer.twitter.com/en/docs/tweets/filter-realtime/guides/basic-stream-parameters.html.

  7. 7.

    https://networkx.github.io/.

  8. 8.

    https://botometer.iuni.iu.edu.

  9. 9.

    https://developer.twitter.com/en/support/twitter-api/error-troubleshooting.

  10. 10.

    https://help.twitter.com/en/managing-your-account/suspended-twitter-accounts.

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Acknowledgement

We are grateful to Twitter for providing access to the trolls’ ground truth dataset. We thank Nikolaos Laoutaris for his insightful comments about the Shapley Value. This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Cybersecurity CONCORDIA project (Grant Agreement No. 830927) and under the Marie Skłodowska-Curie INCOGNITO project (Grant Agreement No. 824015).

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Authors and Affiliations

  1. Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, 3036, Cyprus

    Nikos Salamanos, Costas Iordanou & Michael Sirivianos

  2. Institute for Governance and Policy Analysis, University of Canberra, Canberra, 2601, Australia

    Michael J. Jensen

Authors
  1. Nikos Salamanos
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  2. Michael J. Jensen
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  3. Costas Iordanou
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  4. Michael Sirivianos
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Correspondence to Nikos Salamanos .

Editor information

Editors and Affiliations

  1. University of Kent, Canterbury, UK

    Budi Arief

  2. University of Pisa, Pisa, Italy

    Anna Monreale

  3. Cyprus University of Technology, Limassol, Cyprus

    Michael Sirivianos

  4. University of Kent, Canterbury, UK

    Shujun Li

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Salamanos, N., Jensen, M.J., Iordanou, C., Sirivianos, M. (2023). Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter. In: Arief, B., Monreale, A., Sirivianos, M., Li, S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2023. Lecture Notes in Computer Science, vol 14097. Springer, Singapore. https://doi.org/10.1007/978-981-99-5177-2_4

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  • DOI: https://doi.org/10.1007/978-981-99-5177-2_4

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