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Erschienen in:
Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Credibility-Based Fake News Detection

verfasst von : Niraj Sitaula, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, Reza Zafarani

Erschienen in: Disinformation, Misinformation, and Fake News in Social Media

Verlag: Springer International Publishing

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Abstract

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author’s history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.

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Vorheriges Kapitel Exploring the Role of Visual Content in Fake News Detection
Nächstes Kapitel Standing on the Shoulders of Guardians: Novel Methodologies to Combat Fake News
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Metadaten
Titel
Credibility-Based Fake News Detection
verfasst von
Niraj Sitaula
Chilukuri K. Mohan
Jennifer Grygiel
Xinyi Zhou
Reza Zafarani
Copyright-Jahr
2020
Buch
Disinformation, Misinformation, and Fake News in Social Media

Print ISBN: 978-3-030-42698-9

Electronic ISBN: 978-3-030-42699-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-42699-6_9

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