Mitigating Confirmation Bias on Twitter by Recommending Opposing Views

Elisabeth Lex, Mario Wagner, Dominik Kowald

Research output: Contribution to journal › Article › Research › peer-review

Abstract

In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives. Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump.
Original languageEnglish
Article number arXiv:1809.03901
JournalarXiv.org e-Print archive
DOIs
Publication statusPublished - 11 Sep 2018
EventEuropean Symposium on Societal Challenges in Computational Social Science: Bias and Discrimination - Köln, Germany
Duration: 5 Dec 2018 → 7 Dec 2018

Keywords

  • cs.IR
  • cs.SI

Cite this

Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. / Lex, Elisabeth; Wagner, Mario; Kowald, Dominik.

In: arXiv.org e-Print archive, 11.09.2018.

Research output: Contribution to journal › Article › Research › peer-review

Lex, E, Wagner, M & Kowald, D 2018, 'Mitigating Confirmation Bias on Twitter by Recommending Opposing Views' arXiv.org e-Print archive. https://doi.org/https://arxiv.org/abs/1809.03901
Lex E, Wagner M, Kowald D. Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. arXiv.org e-Print archive. 2018 Sep 11. arXiv:1809.03901. https://doi.org/https://arxiv.org/abs/1809.03901
Lex, Elisabeth ; Wagner, Mario ; Kowald, Dominik. / Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. In: arXiv.org e-Print archive. 2018.
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