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Uncovering Discussion Groups on Claims of Election Fraud from Twitter

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Social Informatics (SocInfo 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13618))

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Abstract

Twitter was widely used during the 2020 U.S. election to disseminate claims of election fraud. As a result, a number of works have examined this phenomenon from a variety of perspectives. However, none of them focus on analyzing topics behind the general fraud claims and associating them with user communities. To fill this gap, we propose to uncover and characterize groups of Twitter users engaging in discussions about election fraud claims during the 2020 U.S. election using a large dataset that spans seven weeks during this period. To accomplish this, we model a sequence of co-retweet networks and employ a backbone extraction method that controls for inherent traits of social media applications, particularly, user activity levels and the popularity of tweets (which together generate many spurious edges in the network), thus allowing us to reveal topics of tweets that lead users to retweet them. After extracting the backbones, we identify user groups representative of the communities present in the network backbones and finally analyze the topics behind the retweeted tweets to understand how they contributed to the spread of fraud claims at that time. Our main results show that (i) our approach uncovers better-structured communities than the original network in terms of users spreading discussions about fraud; and (ii) these users discuss 25 topics with specific psycholinguistic and temporal characteristics.

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Notes

  1. 1.

    https://www.nytimes.com/2021/01/11/technology/twitter-removes-70000-qanon-accounts.html.

  2. 2.

    https://www.bbc.com/news/technology-55638558.

  3. 3.

    https://voterfraud2020.io/.

  4. 4.

    The election week began on November \(3^{rd}\) 2020.

  5. 5.

    We consider a week from Sunday to Saturday.

  6. 6.

    https://huggingface.co/sentence-transformers/all-mpnet-base-v2.

  7. 7.

    We have disregarded communities with less than 100 users.

  8. 8.

    https://maartengr.github.io/BERTopic/getting_started/parameter%20tuning/parametertuning.html.

  9. 9.

    We have noted the presence of some fake news according to the Fact Check Agencies. However, we did not analyze this aspect because it is beyond the scope of this paper.

  10. 10.

    https://www.cbc.ca/news/world/trump-supporters-washington-march-1.5802409.

  11. 11.

    https://www.theguardian.com/us-news/2021/dec/28/donald-trump-georgia -2020-election-dead-people.

  12. 12.

    https://www.bbc.com/news/election-us-2020-54874120.

  13. 13.

    https://www.oann.com/.

  14. 14.

    The z-score normalization is defined by \(z = (x - \mu )/\sigma \).

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Acknowledgments

We thank the Voter Fraud Project and Abilov et al. [1] for providing the data to support our work.

Author information

Authors and Affiliations

  1. Department of Computing and Systems, Universidade Federal de Ouro Preto, João Monlevade, Brazil

    Jose Martins da Rosa Jr, Renan Saldanha Linhares & Carlos Henrique Gomes Ferreira

  2. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Carlos Henrique Gomes Ferreira, Gabriel P. Nobre, Fabricio Murai & Jussara M. Almeida

  3. Worcester Polytechnic Institute, Worcester, USA

    Fabricio Murai

Authors
  1. Jose Martins da Rosa Jr
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  2. Renan Saldanha Linhares
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  3. Carlos Henrique Gomes Ferreira
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  4. Gabriel P. Nobre
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  5. Fabricio Murai
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  6. Jussara M. Almeida
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Corresponding author

Correspondence to Jose Martins da Rosa Jr .

Editor information

Editors and Affiliations

  1. Universität Koblenz-Landau, Koblenz, Germany

    Frank Hopfgartner

  2. National University of Singapore, Singapore, Singapore

    Kokil Jaidka

  3. GESIS – Leibniz-Institut für Sozialwissenschaften, Cologne, Germany

    Philipp Mayr

  4. University of Glasgow, Glasgow, UK

    Joemon Jose

  5. University of Glasgow, Glasgow, UK

    Jan Breitsohl

Appendix I

Appendix I

Table 5. Keywords, description and external links related to the extracted topics.
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da Rosa, J.M., Linhares, R.S., Ferreira, C.H.G., Nobre, G.P., Murai, F., Almeida, J.M. (2022). Uncovering Discussion Groups on Claims of Election Fraud from Twitter. In: Hopfgartner, F., Jaidka, K., Mayr, P., Jose, J., Breitsohl, J. (eds) Social Informatics. SocInfo 2022. Lecture Notes in Computer Science, vol 13618. Springer, Cham. https://doi.org/10.1007/978-3-031-19097-1_20

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