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An Empirical Analysis of Intervention Strategies’ Effectiveness for Countering Misinformation Amplification by Recommendation Algorithms

  • Conference paper
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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14611))

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Abstract

Social network platforms connect people worldwide, facilitating communication, information sharing, and personal/professional networking. They use recommendation algorithms to personalize content and enhance user experiences. However, these algorithms can unintentionally amplify misinformation by prioritizing engagement over accuracy. For instance, recent works suggest that popularity-based and network-based recommendation algorithms contribute the most to misinformation diffusion. In our study, we present an exploration on two Twitter datasets to understand the impact of intervention techniques on combating misinformation amplification initiated by recommendation algorithms. We simulate various scenarios and evaluate the effectiveness of intervention strategies in social sciences such as Virality Circuit Breakers and accuracy nudges. Our findings highlight that these intervention strategies are generally successful when applied on top of collaborative filtering and content-based recommendation algorithms, while having different levels of effectiveness depending on the number of users keen to spread fake news present in the dataset.

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Notes

  1. 1.

    Fake news is a form of misinformation consisting of false or misleading information presented as news with the intent of manipulating people’s perceptions of real facts, events, and statements. Although they are distinct terms, in this paper, we use “fake news” and “misinformation” interchangeably.

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Acknowledgments

This research has been supported by the National Science Foundation under Award no. 1943370.

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Authors and Affiliations

  1. Computer Science Department, Boise State University, Boise, ID, USA

    Royal Pathak & Francesca Spezzano

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  1. Royal Pathak
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  2. Francesca Spezzano
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Correspondence to Francesca Spezzano .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Pathak, R., Spezzano, F. (2024). An Empirical Analysis of Intervention Strategies’ Effectiveness for Countering Misinformation Amplification by Recommendation Algorithms. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14611. Springer, Cham. https://doi.org/10.1007/978-3-031-56066-8_23

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  • DOI: https://doi.org/10.1007/978-3-031-56066-8_23

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