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Polarization and Fake News: Early Warning of Potential Misinformation Targets

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Published:27 March 2019Publication History
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Abstract

Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.

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                cover image ACM Transactions on the Web
                ACM Transactions on the Web  Volume 13, Issue 2
                May 2019
                156 pages
                ISSN:1559-1131
                EISSN:1559-114X
                DOI:10.1145/3313948
                Issue’s Table of Contents

                Copyright © 2019 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 27 March 2019
                • Accepted: 1 February 2019
                • Revised: 1 December 2018
                • Received: 1 February 2018
                Published in tweb Volume 13, Issue 2

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