research-article

Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure

Authors:

Emilio FerraraAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 4674 - 4685

https://doi.org/10.1145/3589334.3648154

Published: 13 May 2024 Publication History

Abstract

Misinformation proliferation on social media platforms is a pervasive threat to the integrity of online public discourse. Genuine users, susceptible to others' influence, often unknowingly engage with, endorse, and re-share questionable pieces of information, collectively amplifying the spread of misinformation. In this study, we introduce an empirical framework to investigate users' susceptibility to influence when exposed to unreliable and reliable information sources. Leveraging two datasets on political and public health discussions on Twitter, we analyze the impact of exposure on the adoption of information sources, examining how the reliability of the source modulates this relationship. Our findings provide evidence that increased exposure augments the likelihood of adoption. Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources, a trend that persists even among non-partisan users. Furthermore, the number of exposures needed for adoption varies based on the source credibility, with extreme ends of the spectrum (very high or low credibility) requiring fewer exposures for adoption. Additionally, we reveal that the adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels. Our research offers critical insights for mitigating the endorsement of misinformation by vulnerable users, offering a framework to study the dynamics of content exposure and adoption on social media platforms.

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Cited By

Luceri LBoniardi EFerrara EChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Leveraging Large Language Models to Detect Influence Campaigns on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651912(1459-1467)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651912
Luceri LPant� VBurghardt KFerrara EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on TwitterProceedings of the ACM Web Conference 202410.1145/3589334.3645529(2530-2541)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645529

Index Terms

Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure
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 applications
      1. Social networks

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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May 13 - 17, 2024

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Cited By

Luceri LBoniardi EFerrara EChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Leveraging Large Language Models to Detect Influence Campaigns on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651912(1459-1467)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651912
Luceri LPant� VBurghardt KFerrara EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on TwitterProceedings of the ACM Web Conference 202410.1145/3589334.3645529(2530-2541)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645529

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