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Fighting fake news: a role for computational social science in the fight against digital misinformation

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  • Giovanni Luca Ciampaglia

    (Indiana University Network Science Institute)

Abstract

The massive, uncontrolled, and oftentimes systematic spread of inaccurate and misleading information on the Web and social media poses a major risk to society. Digital misinformation thrives on an assortment of cognitive, social, and algorithmic biases and current countermeasures based on journalistic corrections do not seem to scale up. By their very nature, computational social scientists could play a twofold role in the fight against fake news: first, they could elucidate the fundamental mechanisms that make us vulnerable to misinformation online and second, they could devise effective strategies to counteract misinformation.

Suggested Citation

  • Giovanni Luca Ciampaglia, 2018. "Fighting fake news: a role for computational social science in the fight against digital misinformation," Journal of Computational Social Science, Springer, vol. 1(1), pages 147-153, January.
    • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:1:d:10.1007_s42001-017-0005-6
    DOI: 10.1007/s42001-017-0005-6
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    References listed on IDEAS

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    Cited by:

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    2. Hongbin Hu & Yongbin Wang, 2022. "Research on Convergence Media Consensus Mechanism Based on Blockchain," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    3. Babak Ravandi & Fatma Mili, 2019. "Coherence and polarization in complex networks," Journal of Computational Social Science, Springer, vol. 2(2), pages 133-150, July.
    4. Roger D. Magarey & Christina M. Trexler, 2020. "Information: a missing component in understanding and mitigating social epidemics," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    5. Yang, Ann Shawing, 2020. "Misinformation corrections of corporate news: Corporate clarification announcements," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    6. Seoyong Kim & Sunhee Kim, 2020. "The Crisis of Public Health and Infodemic: Analyzing Belief Structure of Fake News about COVID-19 Pandemic," Sustainability, MDPI, vol. 12(23), pages 1-23, November.
    7. Tobias Blanke & Tommaso Venturini, 2022. "A network view on reliability: using machine learning to understand how we assess news websites," Journal of Computational Social Science, Springer, vol. 5(1), pages 69-88, May.
    8. Suzanne Elayan & Martin Sykora, 2024. "Digital intermediaries in pandemic times: social media and the role of bots in communicating emotions and stress about Coronavirus," Journal of Computational Social Science, Springer, vol. 7(3), pages 2481-2504, December.
    9. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.

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