Mitigating Confirmation Bias on Twitter by Recommending Opposing Views

Elisabeth Lex; Mario Wagner; Dominik Kowald

doi:https://arxiv.org/abs/1809.03901

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.

Language	English
Article number	arXiv:1809.03901
Journal	arXiv.org e-Print archive
DOIs	https://arxiv.org/abs/1809.03901
Status	Published - 11 Sep 2018
Event	European 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

Lex, E., Wagner, M., & Kowald, D. (2018). Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. arXiv.org e-Print archive, [ arXiv:1809.03901]. DOI: https://arxiv.org/abs/1809.03901

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. DOI: 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. Available from, DOI: 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

@article{344b202e124f404185dd391bd0218ef6,

title = "Mitigating Confirmation Bias on Twitter by Recommending Opposing Views",

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.",

keywords = "cs.IR, cs.SI",

author = "Elisabeth Lex and Mario Wagner and Dominik Kowald",

note = "European Symposium on Computational Social Science, Cologne, Germany",

year = "2018",

month = "9",

day = "11",

doi = "https://arxiv.org/abs/1809.03901",

language = "English",

journal = "arXiv.org e-Print archive",

publisher = "Cornell University Library",

}

TY - JOUR

T1 - Mitigating Confirmation Bias on Twitter by Recommending Opposing Views

AU - Lex,Elisabeth

AU - Wagner,Mario

AU - Kowald,Dominik

N1 - European Symposium on Computational Social Science, Cologne, Germany

PY - 2018/9/11

Y1 - 2018/9/11

N2 - 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.

AB - 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.

KW - cs.IR

KW - cs.SI

U2 - https://arxiv.org/abs/1809.03901

DO - https://arxiv.org/abs/1809.03901

M3 - Article

JO - arXiv.org e-Print archive

T2 - arXiv.org e-Print archive

JF - arXiv.org e-Print archive

M1 - arXiv:1809.03901

ER -

Access to Document

https://arxiv.org/abs/1809.03901