Skip to main content
SpringerLink
Log in

Algorithmic Amplification of Politics and Engagement Maximization on Social Media

  • Conference paper
  • First Online:
Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1144))

Included in the following conference series:

Abstract

This study examines how engagement-maximizing recommender systems influence the visibility of Members of Parliament’s tweets in timelines. Leveraging engagement predictive models and Twitter data, we evaluate various recommender systems. Our analysis reveals that prioritizing engagement decreases the ideological diversity of the audiences reached by Members of Parliament and increases the reach disparities between political groups. When evaluating the algorithmic amplification within the general population, engagement-based timelines confer greater advantages to mainstream right-wing parties compared to their left-wing counterparts. However, when considering users’ individual political leanings, engagement-based timelines amplify ideologically aligned content. We stress the need for audits accounting for user characteristics when assessing the distortions introduced by personalization algorithms and advocate addressing online platform regulations by directly evaluating the metrics platforms aim to optimize, beyond the mere algorithmic implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huszár, F., Ktena, S., O’Brien, C., Belli, L., Schlaikjer, A., Hardt, M.: Algorithmic amplification of politics on Twitter. Proc. Natl. Acad. Sci. U.S.A. 119(1), e2025334119 (2021 12). https://doi.org/10.1073%252Fpnas.2025334119

  2. Kmetty, Z., et al.: Determinants of willingness to donate data from social media platforms. (Center for Open Science, 2023, 3). https://doi.org/10.31219%252Fosf.io%252Fncwkt

  3. Belli, L. et al.: Privacy-Aware Recommender Systems Challenge on Twitter’s Home Timeline (2020)

    Google Scholar 

  4. Belli, L. el at.: The 2021 RecSys Challenge Dataset: Fairness is not optional. In: RecSysChallenge ’21: Proceedings Of The Recommender Systems Challenge 2021. (2021 10). https://doi.org/10.1145%252F3487572.3487573

  5. Satuluri, V., et al.: Proceedings of The 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020 8). https://doi.org/10.1145%252F3394486.3403370

  6. Bouchaud, P. Skewed perspectives: Examining the Influence of Engagement Maximization on Content Diversity in Social Media Feeds. (2023 6). https://hal.science/hal-04139494 preprint

  7. Jolly, S., et al.: Chapel hill expert survey trend file, 1999–2019. Electoral Stud. 75 102420 (2022 2). https://doi.org/10.1016%252Fj.electstud.2021.102420

  8. Rathje, S., Bavel, J., Linden, S.: Out-group animosity drives engagement on social media. Proc. Natl. Acad. Sci. U.S.A. 118 (2021 6). https://doi.org/10.1073%252Fpnas.2024292118

  9. Ribeiro, M., Veselovsky, V., West, R.: The Amplification Paradox in Recommender Systems (2023)

    Google Scholar 

  10. Chavalarias, D., Bouchaud, P., Panahi, M.: Can a single line of code change society? the systemic risks of optimizing engagement in recommender systems on global information flow, opinion dynamics and social structures. J. Artif. Soc. Soc. Simul. 27(1), 9 (2024). https://doi.org/10.18564/jasss.5203

  11. Rossi, W., Polderman, J., Frasca, P.: The closed loop between opinion formation and personalized recommendations. IEEE Trans. Control Netw. Syst. Trans. Contr. Netw. Syst. 9, 1092–1103 (2022 9). https://doi.org/10.1109%252Ftcns.2021.3105616

  12. Bouchaud, P., Chavalarias, D., Panahi, M.: Crowdsourced audit of Twitter’s recommender systems. Sci. Rep. 13, 16815 (2023). https://doi.org/10.1038/s41598-023-43980-4

  13. Milli, S., Carroll, M., Pandey, S., Wang, Y., Dragan, A. Twitter’s Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023)

    Google Scholar 

  14. Bavel, J., Rathje, S., Harris, E., Robertson, C., Sternisko, A.: How social media shapes polarization. Trends in Cogn. Sci. 25, 913–916 (2021 11). https://doi.org/10.1016%252Fj.tics.2021.07.013

  15. Grover, A., Leskovec, J.: node2vec. In: Proceedings Of The 22nd ACM SIGKDD International Conference On Knowledge Discovery and Data Mining. (2016 8). https://doi.org/10.1145%252F2939672.2939754

  16. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30 (NIP 2017). (2017,12)

    Google Scholar 

  17. Barbiero, P., Squillero, G., Tonda, A.: Modeling generalization in machine learning: a methodological and computational study (2020)

    Google Scholar 

  18. Milli, S., Pierson, E., Garg, N.: Balancing Value, Strategy, and Noise in Recommender Systems, Choosing the Right Weights (2023)

    Google Scholar 

  19. Gaumont, N., Panahi, M., Chavalarias, D.: Reconstruction of the socio-semantic dynamics of political activist Twitter networks-Method and application to the 2017 French presidential election. PLoS ONE ONE. 13, e0201879 (2018 9). https://doi.org/10.1371%252Fjournal.pone.0201879

  20. Hargreaves, E., Agosti, C., Menasche, D., Neglia, G., Reiffers-Masson, A., Altman, E.: Biases in the facebook news feed: a case study on the Italian elections. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis And Mining (ASONAM) (2018 8). https://doi.org/10.1109%5C%252Fasonam.2018.8508659

  21. Brady, W., Wills, J., Jost, J., Tucker, J., Bavel, J.: Emotion shapes the diffusion of moralized content in social networks. Proc. Natl. Acad. Sci. U.S.A. 114, 7313–7318 (2017 6). https://doi.org/10.1073%252Fpnas.1618923114

  22. Bartley, N., Abeliuk, A., Ferrara, E., Lerman, K.: Auditing algorithmic bias on twitter. In: 13th ACM Web Science Conference 2021 (2021 6). https://doi.org/10.1145%252F3447535.3462491

  23. Bandy, J., Diakopoulos, N.: More accounts, fewer links. Proc. ACM Hum.-Comput. Interact. On Human-Computer Interaction. 5, 1–28 (2021 4). https://doi.org/10.1145%5C%252F3449152

  24. Guess, A., et al.: How do social media feed algorithms affect attitudes and behavior in an election campaign? Science. 381, 398–404 (2023 7). https://doi.org/10.1126%252Fscience.abp9364

  25. Jacomy, M., Venturini, T., Heymann, S., Bastian, M.: ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE ONE. 9, e98679 (2014 6). https://doi.org/10.1371%252Fjournal.pone.0098679

  26. Twitter TweepCred. GitHub. https://github.com/twitter/the-algorithm/blob/main/src/scala/com/twitter/graph/batch/job/tweepcred

  27. Twitter Source Code for Twitter’s recommendation algorithm: Heavy Ranker. GitHub. https://github.com/twitter/the-algorithm-ml/blob/main/projects/home/recap

  28. Twitter Twitter/the-Algorithm: Source Code for Twitter’s recommendation algorithm. GitHub. https://github.com/twitter/the-algorithm

  29. Twitter Twitter’s recommendation algorithm. Twitter. https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

  30. Twitter What Twitter learned from the Recsys 2020 challenge. Twitter. https://blog.twitter.com/engineering/en_us/topics/insights/2020/what_twitter_learned_from_recsys2020

Download references

Acknowledgments

The author deeply thanks Pedro Ramaciotti Morales for his precious insights and Mazyiar Panahi for enabling the collection of the large-scale retweet network. Finally, the author acknowledges the Jean-Pierre Aguilar fellowship from the CFM Foundation for Research, the support and resources provided by the Complex Systems Institute of Paris Île-de-France.

Author information

Authors and Affiliations

  1. École des Hautes Études En Sciences Sociales, Paris, France

    Paul Bouchaud

  2. Institut des Systèmes Complexes Paris Île-de-France, Paris, France

    Paul Bouchaud

Authors
  1. Paul Bouchaud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Bouchaud .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bouchaud, P. (2024). Algorithmic Amplification of Politics and Engagement Maximization on Social Media. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53503-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53502-4

  • Online ISBN: 978-3-031-53503-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation