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Credibility-Based Fake News Detection

  • Niraj SitaulaEmail author
  • Chilukuri K. Mohan
  • Jennifer Grygiel
  • Xinyi Zhou
  • Reza Zafarani
Chapter
  • 48 Downloads
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author’s history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.

Keywords

Fake news Misinformation Credibility assessment Social media Data analysis 

References

  1. 1.
    Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., Nakov, P.: Predicting factuality of reporting and bias of news media sources. arXiv preprint arXiv:1810.01765 (2018)Google Scholar
  2. 2.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text With the Natural Language Toolkit. O’Reilly Media, Inc, Beijing (2009)Google Scholar
  3. 3.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)Google Scholar
  4. 4.
    Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS One 10(6), e0128193 (2015)CrossRefGoogle Scholar
  5. 5.
    Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 171–175. Association for Computational Linguistics (2012)Google Scholar
  6. 6.
    Fletcher, R., Nielsen, R.K.: Are people incidentally exposed to news on social media? a comparative analysis. New Media Soc. 20(7), 2450–2468 (2018)CrossRefGoogle Scholar
  7. 7.
    Guess, A., Nagler, J., Tucker, J.: Less than you think: prevalence and predictors of fake news dissemination on facebook. Sci. Adv. 5(1), eaau4586 (2019)Google Scholar
  8. 8.
    Gupta, A., Kumaraguru, P.: Twitter explodes with activity in Mumbai blasts! a lifeline or an unmonitored daemon in the lurking? Technical report (2012)Google Scholar
  9. 9.
    Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: Tweetcred: real-time credibility assessment of content on twitter. In: International Conference on Social Informatics, pp. 228–243. Springer (2014)Google Scholar
  10. 10.
    Hassan, N., Arslan, F., Li, C., Tremayne, M.: Toward automated fact-checking: detecting check-worthy factual claims by claimbuster. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1803–1812. ACM (2017)Google Scholar
  11. 11.
    Horne, B.D., Adali, S.: This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Eleventh International AAAI Conference on Web and Social Media (2017)Google Scholar
  12. 12.
    Hunter, J.D.: Matplotlib: A 2d graphics environment. Comput. Sci. Eng. 9(3), 90 (2007)CrossRefGoogle Scholar
  13. 13.
    Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)Google Scholar
  14. 14.
    Jin, Z., Cao, J., Zhang, Y., Zhou, J., Tian, Q.: Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimedia 19(3), 598–608 (2017)CrossRefGoogle Scholar
  15. 15.
    Karimi, H., Tang, J.: Learning hierarchical discourse-level structure for fake news detection. arXiv preprint arXiv:1903.07389 (2019)Google Scholar
  16. 16.
    Koetsenruijter, A.W.M.: Using numbers in news increases story credibility. Newspaper Res. J. 32(2), 74–82 (2011)CrossRefGoogle Scholar
  17. 17.
    McKinney W.: Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51–56 (2010)Google Scholar
  18. 18.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)Google Scholar
  19. 19.
    Morris, M.R., Counts, S., Roseway, A., Hoff, A., Schwarz, J.: Tweeting is believing? understanding microblog credibility perceptions. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 441–450. ACM (2012)Google Scholar
  20. 20.
    Newman, M.E.: Coauthorship networks and patterns of scientific collaboration. Proc. Natl. Acad. Sci. 101(suppl 1), 5200–5205 (2004)CrossRefGoogle Scholar
  21. 21.
    O’Donovan, J., Kang, B., Meyer, G., Höllerer, T., Adalii, S.: Credibility in context: an analysis of feature distributions in twitter. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 293–301. IEEE (2012)Google Scholar
  22. 22.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Pérez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)Google Scholar
  24. 24.
    Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., Stein, B.: A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638 (2017)Google Scholar
  25. 25.
    Rubin, V.L., Lukoianova, T.: Truth and deception at the rhetorical structure level. J. Assoc. Inf. Sci. Technol. 66(5), 905–917 (2015)CrossRefGoogle Scholar
  26. 26.
    Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. Knowl. Based Syst. 104, 123–133 (2016)CrossRefGoogle Scholar
  27. 27.
    Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:1809.01286 (2018)Google Scholar
  28. 28.
    Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsletter 19(1), 22–36 (2017)CrossRefGoogle Scholar
  29. 29.
    Shu, K., Wang, S., Liu, H.: Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 312–320. ACM (2019)Google Scholar
  30. 30.
    Silverman, C.: This analysis shows how viral fake election news stories outperformed real news on facebook (2016). https://www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook#.vtQpz9DKd
  31. 31.
    Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)CrossRefGoogle Scholar
  32. 32.
    Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., Gao, J.: Eann: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 849–857. ACM (2018)Google Scholar
  33. 33.
    Wogalter, M.S., Kalsher, M.J., Rashid, R.: Effect of signal word and source attribution on judgments of warning credibility and compliance likelihood. Int. J. Ind. Ergon. 24(2), 185–192 (1999)CrossRefGoogle Scholar
  34. 34.
    Zafarani, R., Abbasi, M.A., Liu, H.: Social media mining: an introduction. Cambridge University Press, New York (2014)CrossRefGoogle Scholar
  35. 35.
    Zhang, J., Cui, L., Fu, Y., Gouza, F.B.: Fake news detection with deep diffusive network model. arXiv preprint arXiv:1805.08751 (2018)Google Scholar
  36. 36.
    Zhou, X., Jain, A., Phoha, V.V., Zafarani, R.: Fake news early detection: a theory-driven model. arXiv preprint arXiv:1904.11679 (2019)Google Scholar
  37. 37.
    Zhou, X., Zafarani, R.: Fake news: a survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315 (2018)Google Scholar
  38. 38.
    Zhou, X., Zafarani, R.: Network-based fake news detection: a pattern-driven approach. arXiv preprint arXiv:1906.04210 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Niraj Sitaula
    • 1
    Email author
  • Chilukuri K. Mohan
    • 1
  • Jennifer Grygiel
    • 1
  • Xinyi Zhou
    • 1
  • Reza Zafarani
    • 1
  1. 1.Syracuse UniversitySyracuseUSA

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