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Computational Fact Checking from Knowledge Networks

Author

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  • Giovanni Luca Ciampaglia
  • Prashant Shiralkar
  • Luis M Rocha
  • Johan Bollen
  • Filippo Menczer
  • Alessandro Flammini

Abstract

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.

Suggested Citation

  • Giovanni Luca Ciampaglia & Prashant Shiralkar & Luis M Rocha & Johan Bollen & Filippo Menczer & Alessandro Flammini, 2015. "Computational Fact Checking from Knowledge Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0128193
    DOI: 10.1371/journal.pone.0128193
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    Cited by:

    1. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.
    2. Bessi, Alessandro, 2017. "On the statistical properties of viral misinformation in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 459-470.
    3. 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.
    4. Abhishek Samantray & Paolo Pin, 2019. "Credibility of climate change denial in social media," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-8, December.
    5. Janne Kauttonen & Jenni Hannukainen & Pia Tikka & Jyrki Suomala, 2020. "Predictive modeling for trustworthiness and other subjective text properties in online nutrition and health communication," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-23, August.
    6. Marcella Tambuscio & Diego F. M. Oliveira & Giovanni Luca Ciampaglia & Giancarlo Ruffo, 2018. "Network segregation in a model of misinformation and fact-checking," Journal of Computational Social Science, Springer, vol. 1(2), pages 261-275, September.
    7. Ángel Vizoso & Martín Vaz-Álvarez & Xosé López-García, 2021. "Fighting Deepfakes: Media and Internet Giants’ Converging and Diverging Strategies Against Hi-Tech Misinformation," Media and Communication, Cogitatio Press, vol. 9(1), pages 291-300.
    8. 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.

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