Factors in Recommending Contrarian Content on Social Media

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A4 Artikkeli konferenssijulkaisussa
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2017-06-25

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Department of Computer Science

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en

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4

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WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference, pp. 263-266

Abstract

Polarization is a troubling phenomenon that can lead to societal divisions and hurt the democratic process. It is therefore important to develop methods to reduce it. We propose an algorithmic solution to the problem of reducing polarization. The core idea is to expose users to content that challenges their point of view, with the hope broadening their perspective, and thus reduce their polarity. Our method takes into account several aspects of the problem, such as the estimated polarity of the user, the probability of accepting the recommendation, the polarity of the content, and popularity of the content being recommended. We evaluate our recommendations via a large-scale user study on Twitter users that were actively involved in the discussion of the US elections results. Results shows that, in most cases, the factors taken into account in the recommendation affect the users as expected, and thus capture the essential features of the problem.

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| openaire: EC/H2020/654024/EU//SoBigData

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DOI

10.1145/3091478.3091515

Citation

Garimella, V, De Francisci Morales, G, Gionis, G & Mathioudakis, M 2017, Factors in Recommending Contrarian Content on Social Media. in WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. ACM, pp. 263-266, ACM Web Science Conference, Troy, New York, United States, 25/06/2017. https://doi.org/10.1145/3091478.3091515

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https://urn.fi/URN:NBN:fi:aalto-201809064982

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[article-cris] Perustieteiden korkeakoulu / SCI