Fighting fake news: a role for computational social science in the fight against digital misinformation
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DOI: 10.1007/s42001-017-0005-6
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Cited by:
- 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.
- 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.
- Emilio Ferrara & Stefano Cresci & Luca Luceri, 2020. "Misinformation, manipulation, and abuse on social media in the era of COVID-19," Journal of Computational Social Science, Springer, vol. 3(2), pages 271-277, November.
- 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.
- Hongbin Hu & Yongbin Wang, 2022. "Research on Convergence Media Consensus Mechanism Based on Blockchain," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
- Yang, Ann Shawing, 2020. "Misinformation corrections of corporate news: Corporate clarification announcements," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
- Babak Ravandi & Fatma Mili, 2019. "Coherence and polarization in complex networks," Journal of Computational Social Science, Springer, vol. 2(2), pages 133-150, July.
- 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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Keywords
Digital misinformation; Algorithmic bias; Fact checking;All these keywords.
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