Differences in health news from reliable and unreliable media

Sameer Dhoju, Md Main Uddin Rony, Ashad Kabir, Naeemul Hassan

Research output: Book chapter/Published conference paperConference paper

2 Citations (Scopus)

Abstract

The spread of ‘fake’ health news is a big problem with even bigger consequences. In this study, we examine a collection of health-related news articles published by reliable and unreliable media outlets. Our analysis shows that there are structural, topical, and semantic patterns which are different in contents from reliable and unreliable media outlets. Using machine learning, we leverage these patterns and build classification models to identify the source (reliable or unreliable) of a health-related news article. Our model can predict the source of an article with an F-measure of 96%. We argue that the findings from this study will be useful for combating the health disinformation problem.
Original languageEnglish
Title of host publicationThe Web Conference 2019
Subtitle of host publicationCompanion of The World Wide Web Conference WWW 2019
EditorsSihem Amer-Yahia, Mohammad Madian, Ashish Goel, Geert-Jan Houben, Kristina Lerman, Julian McAuley, Ricardo Baeza-Yates, Leila Zia
Place of PublicationUSA
PublisherAssociation for Computing Machinery (ACM)
Pages981-987
Number of pages7
ISBN (Print)9781450366755
DOIs
Publication statusPublished - May 2019
EventThe Web Conference 2019 - The Hyatt Regency, San Francisco, United States
Duration: 13 May 201917 May 2019
https://www2019.thewebconf.org/

Conference

ConferenceThe Web Conference 2019
CountryUnited States
CitySan Francisco
Period13/05/1917/05/19
Internet address

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  • Cite this

    Dhoju, S., Rony, M. M. U., Kabir, A., & Hassan, N. (2019). Differences in health news from reliable and unreliable media. In S. Amer-Yahia, M. Madian, A. Goel, G-J. Houben, K. Lerman, J. McAuley, R. Baeza-Yates, & L. Zia (Eds.), The Web Conference 2019: Companion of The World Wide Web Conference WWW 2019 (pp. 981-987). Association for Computing Machinery (ACM). https://doi.org/10.1145/3308560.3316741