Computational Fact Checking from Knowledge Networks
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DOI: 10.1371/journal.pone.0128193
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Cited by:
- 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.
- 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.
- 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.
- Abhishek Samantray & Paolo Pin, 2019. "Credibility of climate change denial in social media," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-8, December.
- 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.
- 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.
- 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.
- 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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