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Leveraging Heterogeneous Data for Fake News Detection

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Linking and Mining Heterogeneous and Multi-view Data

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Nowadays, a plenty of social media platforms are available to exchange information rapidly. Such a rapid propagation and cumulation of information form a deluge, in which it is hard to believe all the pieces of information since it appears to be very realistic. In this context, characterizing and recognizing misinformation, especially, fake news, is a highly recommended computational task. News fabrication mostly happens through the textual and visual content comprised in the news article. People spreading fake news have been intentionally modifying the content of a news with some partially true information or use fully manipulated information, newly fabricated stories, etc., which could mislead others. Fake news characterization and detection are the computational studies that focus to get rid of the highly malicious news creation and propagation. The textual and visual content-related features, temporal and propagation patterns of the network, that use traditional and deep neural computations are the methods to identify fake news generation and spread. This chapter discusses the methods to leverage heterogeneous data to curb the fake news generation and propagation. We present an extensive review of the state-of-the-art fake news detection systems, in the context of different modalities emphasizing the content-based approaches including text and image modality and also discuss briefly the network, temporal, and knowledge base approaches. This study also extends to discuss the available datasets in this area, the open issues, and future directions of research.

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Notes

  1. 1.

    https://www.macquariedictionary.com.au/news/view/article/431/.

  2. 2.

    https://en.oxforddictionaries.com/word-of-the-year/word-of-the-year-2016.

  3. 3.

    http://blogs.channel4.com/factcheck/.

  4. 4.

    http://www.politifact.com/.

  5. 5.

    http://webarchive.nationalarchives.gov.uk/20120203152804/http://www.tate.org.uk/tateetc/issue8/erasurerevelation.htm.

  6. 6.

    https://www.snopes.com/fact-check/nepal-earthquake-photo/.

  7. 7.

    http://www.chinadaily.com.cn/china/2013-03/29/content_16357003.htm.

  8. 8.

    https://www.snopes.com/fact-check/7-headed-snake/.

  9. 9.

    http://news.yahoo.com/10-fake-photos-hurricane-sandy-075500934.html.

  10. 10.

    http://service.account.weibo.com/.

  11. 11.

    https://fullfact.org/.

  12. 12.

    http://factchecker.in/.

  13. 13.

    https://reporterslab.org/.

  14. 14.

    http://fiskkit.com/.

  15. 15.

    https://www.cs.ucsb.edu/~william/data/liar_dataset.zip.

  16. 16.

    https://sites.google.com/site/andreasvlachos/resources.

  17. 17.

    https://github.com/BuzzFeedNews/2016-10-facebook-fact-check.

  18. 18.

    https://www.kaggle.com/mrisdal/fake-news.

  19. 19.

    https://github.com/selfagency/bs-detector.

  20. 20.

    http://compsocial.github.io/CREDBANK-data/.

  21. 21.

    https://github.com/KaiDMML/FakeNewsNet.

  22. 22.

    https://github.com/FakeNewsChallenge/fnc-1.

  23. 23.

    https://snap.stanford.edu/data/higgs-twitter.html.

  24. 24.

    http://mklab.iti.gr/project/wild-web-tampered-image-dataset.

  25. 25.

    http://forensics.idealtest.org/.

  26. 26.

    http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.

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  1. Department of Computer Science, University of Calicut, Malappuram, Kerala, India

    K. Anoop, Manjary P. Gangan & V. L. Lajish

  2. Queen’s University Belfast, Belfast, UK

    Deepak P

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Anoop, K., Gangan, M.P., P, D., Lajish, V.L. (2019). Leveraging Heterogeneous Data for Fake News Detection. In: P, D., Jurek-Loughrey, A. (eds) Linking and Mining Heterogeneous and Multi-view Data. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-01872-6_10

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