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Evaluating the Role of News Content and Social Media Interactions for Fake News Detection

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Disinformation in Open Online Media (MISDOOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12887))

Abstract

Societies across the globe suffer from the effects of disinformation campaigns creating an urgent need for a way of tracking falsehoods before they become widely spread. Although building a detection tool for online disinformation campaigns is a challenging task, this paper attempts to approach this problem by examining content-based features related to language use, emotions, and engagement features through explainable machine learning. We propose a model that, except for the textual attributes, harnesses the predictive power of the users’ interactions on the Facebook platform, and forecasts deceptive content in (i) news articles and in (ii) Facebook news-related posts. The findings of the study show that the proposed model is able to predict misleading news stories with a 98% accuracy based on features such as capitals in the main body, headline length, Facebook likes, the total amount of nouns and numbers, lexical diversity, and arousal. In conclusion, the paper provides new insights concerning the false news identifiers crucial for both news publishers and consumers.

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Notes

  1. 1.

    https://www.bbc.com/news/world-53755067.

  2. 2.

    https://www.poynter.org/coronavirusfactsalliance/.

  3. 3.

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

  4. 4.

    https://github.com/gsantia/BuzzFace.

  5. 5.

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

  6. 6.

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

  7. 7.

    https://github.com/gabll/some-like-it-hoax.

  8. 8.

    https://www.politifact.com/article/2017/apr/20/politifacts-guide-fake-news-websites-and-what-they/.

  9. 9.

    https://en.wikipedia.org/wiki/List_of_fake_news_websites.

  10. 10.

    https://pypi.org/project/py-readability-metrics/.

  11. 11.

    https://www.nltk.org/.

  12. 12.

    https://scikit-learn.org/stable/.

  13. 13.

    https://eli5.readthedocs.io/en/latest/overview.html.

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Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant (GA. 14540).

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Authors and Affiliations

  1. Faculty of Communication and Media Studies, National and Kapodistrian University of Athens, Athens, Greece

    Catherine Sotirakou, Anastasia Karampela & Constantinos Mourlas

Authors
  1. Catherine Sotirakou
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  2. Anastasia Karampela
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  3. Constantinos Mourlas
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Corresponding author

Correspondence to Catherine Sotirakou .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Jonathan Bright

  2. Utrecht University, Utrecht, The Netherlands

    Anastasia Giachanou

  3. University of Leeds, Leeds, UK

    Viktoria Spaiser

  4. Boise State University, Boise, ID, USA

    Francesca Spezzano

  5. University of Oxford, Oxford, UK

    Anna George

  6. University of Oxford, Oxford, UK

    Alexandra Pavliuc

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Sotirakou, C., Karampela, A., Mourlas, C. (2021). Evaluating the Role of News Content and Social Media Interactions for Fake News Detection. In: Bright, J., Giachanou, A., Spaiser, V., Spezzano, F., George, A., Pavliuc, A. (eds) Disinformation in Open Online Media. MISDOOM 2021. Lecture Notes in Computer Science(), vol 12887. Springer, Cham. https://doi.org/10.1007/978-3-030-87031-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-87031-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87030-0

  • Online ISBN: 978-3-030-87031-7

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