short-paper

Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?

Authors:

Antonela Tommasel,

Filippo MenczerAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 550 - 555

https://doi.org/10.1145/3523227.3551473

Published: 13 September 2022 Publication History

Abstract

Recommender systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships. However, recommenders could also (unintendedly) help propagate misinformation and increase the social influence of the spreading it. In this context, we study the impact of friend recommender systems on the social influence of misinformation spreaders on Twitter. To this end, we applied several user recommenders to a COVID-19 misinformation data collection. Then, we explore what-if scenarios to simulate changes in user misinformation spreading behaviour as an effect of the interactions in the recommended network. Our study shows that recommenders can indeed affect how misinformation spreaders interact with other users and influence them.

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Cited By

Phillips NKalvapalle SKennedy M(2024)Beyond the Turing Test: Exploring the implications of generative AI for category constructionOrganization Theory10.1177/263178772412751135:3Online publication date: 13-Sep-2024
https://doi.org/10.1177/26317877241275113
Zhang YHu ZBai YWu JWang QFeng F(2024)Recommendation Unlearning via Influence FunctionACM Transactions on Recommender Systems10.1145/37017633:2(1-23)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3701763
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
Show More Cited By

Index Terms

Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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Short-paper
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Refereed limited

Funding Sources

Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación
Consejo Nacional de Investigaciones Científicas y Técnicas

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RecSys '22

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Phillips NKalvapalle SKennedy M(2024)Beyond the Turing Test: Exploring the implications of generative AI for category constructionOrganization Theory10.1177/263178772412751135:3Online publication date: 13-Sep-2024
https://doi.org/10.1177/26317877241275113
Zhang YHu ZBai YWu JWang QFeng F(2024)Recommendation Unlearning via Influence FunctionACM Transactions on Recommender Systems10.1145/37017633:2(1-23)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3701763
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
Duskin K(2024)Stemming the Tide of Problematic Information in Online Environments: Assessing Interventions and Identifying Opportunities for InterruptionCompanion Publication of the 16th ACM Web Science Conference10.1145/3630744.3658615(37-41)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3658615
Hayes PFitzpatrick NFerrández J(2024)From applied ethics and ethical principles to virtue and narrative in AI practicesAI and Ethics10.1007/s43681-024-00472-zOnline publication date: 8-Apr-2024
https://doi.org/10.1007/s43681-024-00472-z
Gravino PPrevedello GBrugnoli E(2024)Online news ecosystem dynamics: supply, demand, diffusion, and the role of disinformationApplied Network Science10.1007/s41109-024-00643-19:1Online publication date: 19-Jul-2024
https://doi.org/10.1007/s41109-024-00643-1
Shrestha ADuran JSpezzano FSerra E(2023)Joint Credibility Estimation of News, User, and Publisher via Role-relational Graph Convolutional NetworksACM Transactions on the Web10.1145/361741818:1(1-24)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3617418
Pathak RSpezzano FPera M(2023)Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social NetworksACM Transactions on the Web10.1145/361608817:4(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3616088
Alshehri MZhang X(2023)Forgetting User Preference in Recommendation Systems with Label-Flipping2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386603(271-281)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386603
Bonicalzi SDe Caro MGiovanola B(2023)Artificial Intelligence and Autonomy: On the Ethical Dimension of Recommender SystemsTopoi10.1007/s11245-023-09922-542:3(819-832)Online publication date: 18-May-2023
https://doi.org/10.1007/s11245-023-09922-5

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