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Detecting fake news for reducing misinformation risks using analytics approaches

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  • Zhang, Chaowei
  • Gupta, Ashish
  • Kauten, Christian
  • Deokar, Amit V.
  • Qin, Xiao

Abstract

Fake news is playing an increasingly dominant role in spreading misinformation by influencing people’s perceptions or knowledge to distort their awareness and decision-making. The growth of social media and online forums has spurred the spread of fake news causing it to easily blend with truthful information. This study provides a novel text analytics–driven approach to fake news detection for reducing the risks posed by fake news consumption. We first describe the framework for the proposed approach and the underlying analytical model including the implementation details and validation based on a corpus of news data. We collect legitimate and fake news, which is transformed from a document based corpus into a topic and event–based representation. Fake news detection is performed using a two-layered approach, which is comprised of detecting fake topics and fake events. The efficacy of the proposed approach is demonstrated through the implementation and validation of a novel FakE News Detection (FEND) system. The proposed approach achieves 92.49% classification accuracy and 94.16% recall based on the specified threshold value of 0.6.

Suggested Citation

  • Zhang, Chaowei & Gupta, Ashish & Kauten, Christian & Deokar, Amit V. & Qin, Xiao, 2019. "Detecting fake news for reducing misinformation risks using analytics approaches," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1036-1052.
    • Handle: RePEc:eee:ejores:v:279:y:2019:i:3:p:1036-1052
    DOI: 10.1016/j.ejor.2019.06.022
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    7. Kalgotra, Pankush & Gupta, Ashish & Sharda, Ramesh, 2021. "Pandemic information support lifecycle: Evidence from the evolution of mobile apps during COVID-19," Journal of Business Research, Elsevier, vol. 134(C), pages 540-559.
    8. Stevenson, Matthew & Mues, Christophe & Bravo, Cristián, 2021. "The value of text for small business default prediction: A Deep Learning approach," European Journal of Operational Research, Elsevier, vol. 295(2), pages 758-771.
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    10. 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.
    11. Paul Meddeb & Stefan Ruseti & Mihai Dascalu & Simina-Maria Terian & Sebastien Travadel, 2022. "Counteracting French Fake News on Climate Change Using Language Models," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
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