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A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News

Jasabanta Patro,Sabyasachee Baruah

Published 2021 in Conference of the European Chapter of the Association for Computational Linguistics

ABSTRACT

There is a huge difference between a scientific journal reporting ‘wine consumption might be correlated to cancer’, and a media outlet publishing ‘wine causes cancer’ citing the journal’s results. The above example is a typical case of a scientific statement being exaggerated as an outcome of the rising problem of media manipulation. Given a pair of statements (say one from the source journal article and the other from the news article covering the results published in the journal), is it possible to ascertain with some confidence whether one is an exaggerated version of the other? This paper presents a surprisingly simple yet rational three-step approach that performs best for this task. We solve the task by breaking it into three sub-tasks as follows – (a) given a statement from a scientific paper or press release, we first extract relation phrases (e.g., ‘causes’ versus ‘might be correlated to’) connecting the dependent (e.g., ‘cancer’) and the independent (‘wine’) variable, (b) classify the strength of the relationship phrase extracted and (c) compare the strengths of the relation phrases extracted from the statements to identify whether one statement contains an exaggerated version of the other, and to what extent. Through rigorous experiments, we demonstrate that our simple approach by far outperforms baseline models that compare state-of-the-art embedding of the statement pairs through a binary classifier or recast the problem as a textual entailment task, which appears to be a very natural choice in this settings.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    Conference of the European Chapter of the Association for Computational Linguistics

  • Publication date

    Unknown publication date

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.18653/v1/2021.eacl-main.289
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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