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Analysing the Effect of Recommendation Algorithms on the Spread of Misinformation

Authors:

Miriam Fernandez,

Alejandro Bellogín,

Iván CantadorAuthors Info & Claims

WEBSCI '24: Proceedings of the 16th ACM Web Science Conference

Pages 159 - 169

https://doi.org/10.1145/3614419.3644003

Published: 21 May 2024 Publication History

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Abstract

Recommendation algorithms (RAs) have been pointed out as one of the major culprits of misinformation spreading in the digital sphere.1 However, it is still unclear how these algorithms propagate misinformation, e.g., which particular recommendation approaches are more prone to suggest misinforming items, or which internal parameters of the algorithms could be influencing more on their misinformation propagation capacity. Motivated by this fact, in this work, we present an analysis of the effect of some of the most popular recommendation algorithms on the spread of misinformation on Twitter (X). A set of guidelines on how to adapt these algorithms is provided based on such analysis and a comprehensive review of the research literature.

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Cited By

Pathak RSpezzano FBoratto LGena CMarras MGermanakos PPopescus E(2024)Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social MediaAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664870(80-85)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664870
Pathak RSerra ESpezzano F(2024)Advancing Misinformation Awareness in Recommender Systems for Social Media Information IntegrityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680259(5471-5474)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680259

Index Terms

Analysing the Effect of Recommendation Algorithms on the Spread of Misinformation
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Conferences

WEBSCI '24: Proceedings of the 16th ACM Web Science Conference

May 2024

395 pages

ISBN:9798400703348

DOI:10.1145/3614419

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 21 May 2024

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PaperSession-3_Misinformation & Moderation_einezln_Donnerstag_240605_MiriamFernandez: Analysing the Effect of Recommendation Algorithms on the Spread of Misinformation https://dl.acm.org/doi/10.1145/3614419.3644003#PaperSession-3_Misinformation & Moderation_einezln_Donnerstag_240605_MiriamFernandez.mp4

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Websci '24: 16th ACM Web Science Conference

May 21 - 24, 2024

Stuttgart, Germany

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Cited By

Pathak RSpezzano FBoratto LGena CMarras MGermanakos PPopescus E(2024)Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social MediaAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664870(80-85)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664870
Pathak RSerra ESpezzano F(2024)Advancing Misinformation Awareness in Recommender Systems for Social Media Information IntegrityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680259(5471-5474)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680259

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