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Textual Characteristics of News Title and Body to Detect Fake News: A Reproducibility Study

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Book cover Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

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Abstract

Fake news, a deliberately designed news to mislead others, is becoming a big societal threat with its fast dissemination over the Web and social media and its power to shape public opinion. Many researchers have been working to understand the underlying features that help identify these fake news on the Web. Recently, Horne and Adali found, on a small amount of data, that news title stylistic and linguistic features are better than the same type of features extracted from the news body in predicting fake news. In this paper, we present our attempt to reproduce the same results to validate their findings. We show which of their findings can be generalized to larger political and gossip news datasets.

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Notes

  1. 1.

    Repetitive language is measured by using the Type-Token Ratio (TTR) which is the number of unique words in the document by the total number of words in the document. A low TTR means more repetitive language, while a high TTR means more lexical diversity. Horne and Adali claim fake news has more repetitive language but show the opposite result in their paper, i.e., TTR is on average higher for fake than real news (cf. Table 4 in [7]), indicating more lexical diversity for fake than real news. Our results confirms more lexical diversity for fake news as shown in Table 2.

  2. 2.

    https://www.politifact.com/.

  3. 3.

    https://www.gossipcop.com/.

  4. 4.

    https://www.eonline.com/ap.

  5. 5.

    The BuzzFeedNews dataset is available at https://zenodo.org/record/1239675#.X5riw0JKgXA.

  6. 6.

    https://github.com/shresthaanu/ECIR21TextualCharacteristicsOfFakeNews.

  7. 7.

    The NRC-EIL lexicon should be downloaded at https://www.saifmohammad.com/WebPages/AffectIntensity.htm.

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Acknowledgements

This work has been supported by the National Science Foundation under Award no. 1943370. We thank Ashlee Milton and Maria Soledad Pera for providing us the code used in their paper [8] to compute emotional features.

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Authors and Affiliations

  1. Computer Science Department, Boise State University, Boise, ID, USA

    Anu Shrestha & Francesca Spezzano

Authors
  1. Anu Shrestha
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  2. Francesca Spezzano
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Corresponding author

Correspondence to Anu Shrestha .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Shrestha, A., Spezzano, F. (2021). Textual Characteristics of News Title and Body to Detect Fake News: A Reproducibility Study. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_9

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