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On Unsupervised Methods for Fake News Detection

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Data Science for Fake News

Part of the book series: The Information Retrieval Series ((INRE,volume 42))

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Abstract

In this chapter, we consider a reasonably underexplored area in fake news analytics, that of unsupervised learning. We intend to keep the narrative accessible to a broader audience than machine learning specialists and accordingly start with outlining the structure of different learning paradigms vis-à-vis supervision. This is followed by an analysis of the challenges that are particularly pertinent for unsupervised fake news detection. Third, we provide an overview of unsupervised learning methods with a focus on their conceptual foundations. We analyze the conceptual bases with a critical eye and outline other kinds of conceptual building blocks that could be used in devising unsupervised fake news detection methods. Fourth, we survey the limited work in unsupervised fake news detection in detail with a methodological focus, outlining their relative strengths and weaknesses. Lastly, we discuss various possible directions in unsupervised fake news detection and consider the challenges and opportunities in the space.

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Authors and Affiliations

  1. Queen’s University Belfast, Belfast, UK

    Deepak P

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  1. Deepak P
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P, D. (2021). On Unsupervised Methods for Fake News Detection. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-62696-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-62696-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62695-2

  • Online ISBN: 978-3-030-62696-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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