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  • Impact of Content Ideology on Social Media Opinion-author accepted

    Rights statement: This is the author’s version of a work that was accepted for publication in Decision Support Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Decision Support Systems, 164, 2022 DOI: 10.1016/j.dss.2022.113845

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Impact of content ideology on social media opinion polarization: The moderating role of functional affordances and symbolic expressions

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Article number113845
<mark>Journal publication date</mark>31/01/2023
<mark>Journal</mark>Decision Support Systems
Volume164
Number of pages13
Publication StatusPublished
Early online date26/07/22
<mark>Original language</mark>English

Abstract

We offer theory and evidence regarding the impact of content ideology (i.e., emotionally charged beliefs expressed in sentiments) on opinion polarization (i.e., conflicting attitudes about an event) on social media. Specifically, we consider the moderating role of functional affordances and symbolic expressions to draw inferences about opinion polarization. From a sentiment analysis of 3600 posts and a survey of 468 Weibo users, we find that content ideology is positively related to social media opinion polarization. The effect of content ideology is greater when users receive stronger symbolic expressions. Further, our results show an insignificant moderating relationship between functional affordances and this effect. The findings suggest that it is critical to consider content ideology and symbolic expressions when assessing the relationship between published content and polarized opinions on social media.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Decision Support Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Decision Support Systems, 164, 2022 DOI: 10.1016/j.dss.2022.113845