Factors in recommending contrarian content on social media

Kiran Garimella, Aristides Gionis, Gianmarco De Francisci Morales, Michael Mathioudakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Pages263-266
Number of pages4
ISBN (Electronic)9781450348966
DOIs
Publication statusPublished - 25 Jun 2017
Event9th ACM Web Science Conference, WebSci 2017 - Troy, United States
Duration: 25 Jun 201728 Jun 2017

Publication series

NameWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference

Other

Other9th ACM Web Science Conference, WebSci 2017
CountryUnited States
CityTroy
Period25/6/1728/6/17

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ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Garimella, K., Gionis, A., De Francisci Morales, G., & Mathioudakis, M. (2017). Factors in recommending contrarian content on social media. In WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference (pp. 263-266). (WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3091478.3091515