Differences in health news from reliable and unreliable media

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

doi:https://doi.org/10.1145/3308560.3316741

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 paper › Conference paper › peer-review

12 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 language	English
Title of host publication	The Web Conference 2019
Subtitle of host publication	Companion of The World Wide Web Conference WWW 2019
Editors	Sihem Amer-Yahia, Mohammad Madian, Ashish Goel, Geert-Jan Houben, Kristina Lerman, Julian McAuley, Ricardo Baeza-Yates, Leila Zia
Place of Publication	USA
Publisher	Association for Computing Machinery (ACM)
Pages	981-987
Number of pages	7
ISBN (Print)	9781450366755
DOIs	https://doi.org/10.1145/3308560.3316741
Publication status	Published - May 2019
Event	The Web Conference 2019 - The Hyatt Regency, San Francisco, United States Duration: 13 May 2019 → 17 May 2019 https://www2019.thewebconf.org/

Conference

Conference	The Web Conference 2019
Abbreviated title	30 years of the web
Country/Territory	United States
City	San Francisco
Period	13/05/19 → 17/05/19
Other	The Web Conference series provides the world with a premier forum about the development of the Web, the standardization of its associated technologies, and the Web’s impact on society and culture. These conferences bring together researchers, developers, users, and vendors – indeed all of you who are passionate about the Web and what it has to offer, now and in the future. Part of what is exciting about this series is the cross boundary and eclectic nature of the topics covered, and the opportunity it provides for exploration of new directions, both technological and social.
Internet address	https://www2019.thewebconf.org/

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https://doi.org/10.1145/3308560.3316741

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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

Dhoju, Sameer ; Rony, Md Main Uddin ; Kabir, Ashad ; Hassan, Naeemul. / Differences in health news from reliable and unreliable media. The Web Conference 2019: Companion of The World Wide Web Conference WWW 2019. editor / Sihem Amer-Yahia ; Mohammad Madian ; Ashish Goel ; Geert-Jan Houben ; Kristina Lerman ; Julian McAuley ; Ricardo Baeza-Yates ; Leila Zia. USA : Association for Computing Machinery (ACM), 2019. pp. 981-987

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title = "Differences in health news from reliable and unreliable media",

abstract = "The spread of {\textquoteleft}fake{\textquoteright} 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.",

author = "Sameer Dhoju and Rony, {Md Main Uddin} and Ashad Kabir and Naeemul Hassan",

year = "2019",

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language = "English",

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pages = "981--987",

editor = "Sihem Amer-Yahia and Mohammad Madian and Ashish Goel and Geert-Jan Houben and Kristina Lerman and Julian McAuley and Ricardo Baeza-Yates and Leila Zia",

booktitle = "The Web Conference 2019",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "The Web Conference 2019, 30 years of the web ; Conference date: 13-05-2019 Through 17-05-2019",

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Dhoju, S, Rony, MMU, 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. Association for Computing Machinery (ACM), USA, pp. 981-987, The Web Conference 2019, San Francisco, California, United States, 13/05/19. https://doi.org/10.1145/3308560.3316741

Differences in health news from reliable and unreliable media. / Dhoju, Sameer ; Rony, Md Main Uddin; Kabir, Ashad; Hassan, Naeemul.

The Web Conference 2019: Companion of The World Wide Web Conference WWW 2019. ed. / Sihem Amer-Yahia; Mohammad Madian; Ashish Goel; Geert-Jan Houben; Kristina Lerman; Julian McAuley; Ricardo Baeza-Yates; Leila Zia. USA : Association for Computing Machinery (ACM), 2019. p. 981-987.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Rony, Md Main Uddin

AU - Kabir, Ashad

AU - Hassan, Naeemul

PY - 2019/5

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N2 - 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.

AB - 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.

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A2 - Amer-Yahia, Sihem

A2 - Madian, Mohammad

A2 - Goel, Ashish

A2 - Houben, Geert-Jan

A2 - Lerman, Kristina

A2 - McAuley, Julian

A2 - Baeza-Yates, Ricardo

A2 - Zia, Leila

PB - Association for Computing Machinery (ACM)

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

Differences in health news from reliable and unreliable media

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