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2021 | OriginalPaper | Chapter

A Classification Algorithm to Recognize Fake News Websites

Authors : Giuseppe Pernagallo, Benedetto Torrisi, Davide Bennato

Published in: Data Science and Social Research II

Publisher: Springer International Publishing

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Abstract

“Fake news” is information that generally spreads on the web, which mimics the form of reliable news media content. In this paper, we use a classifier to distinguish a reliable source from a fake news website based on information potentially available on websites, such as the presence of a “contact us” section or a secured connection. This framework offers a concrete solution to attribute a “reliability score” to news websites, defined as the probability that a source is reliable or not, and based on this probability a user can decide if the news is worth sharing.

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https://www.ft.com/content/a37e4874-2c2a-11e7-bc4b-5528796fe35c.

Hoaxy is a platform promoted by researchers at the Indiana University Network Science Institute (IUNI) and the School of Informatics and Computing’s Center for Complex Networks and Systems Research (CNetS). The platform is available online, and it can be used for the automatic tracking of both online fake news and fact-checking on social media. The goal of the platform is to trace how hoaxes are diffused online and how many users are involved in the process. The interested reader can find further information at https://hoaxy.iuni.iu.edu/faq.php#faq-q1

There are several articles online that report this information such as the page on Wikipedia, “List of fake news websites”, blog.feedspot.com/fake_news_blogs/, usnews.com or the Italian website bufale.net. There are also websites that mimic existing reputable website domain or deliberately share absurd and ironic news.

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Title: A Classification Algorithm to Recognize Fake News Websites
Authors: Giuseppe Pernagallo
Benedetto Torrisi
Davide Bennato
Publisher: Springer International Publishing
Book: Data Science and Social Research II
Print ISBN: 978-3-030-51221-7

Electronic ISBN: 978-3-030-51222-4

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-51222-4_25

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