Does negatively toned language use on social media lead to attitude polarization?

Prior research has indicated that both attitudinal homogeneity of communication networks (“echo chambers”) and attitudinal heterogeneity of communication networks (“adversarial debates”) can lead to attitude polarization. The present paper argues that communication in both echo chambers and adversar...

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Bibliographic Details
Published in:Computers in human behavior 2021-03, Vol.116, p.106663, Article 106663
Main Authors: Buder, Jürgen, Rabl, Lisa, Feiks, Markus, Badermann, Mandy, Zurstiege, Guido
Format: Article
Language:English
Subjects:
Attitude strength
Attitudes
Chambers
Communication
Communication networks
Communications networks
Data mining
Digital media
Echo chamber
Heterogeneity
Homogeneity
Negativity bias
Network analysis
Polarization
Sentiment analysis
Social media
Social networks
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Summary:Prior research has indicated that both attitudinal homogeneity of communication networks (“echo chambers”) and attitudinal heterogeneity of communication networks (“adversarial debates”) can lead to attitude polarization. The present paper argues that communication in both echo chambers and adversarial debates is dominated by network negativity, a negative valence in the tone of discussions which might be associated with attitude polarization. Combining methods from sentiment analysis and social network analysis, more than 4 million tweets on two controversial topics (Brexit, Trump) were analyzed to investigate the occurrence of network negativity and its association with two proxies of attitude polarization (extremity and ambivalence). Results indicate that negativity in users' own tweets was most strongly related to polarization, whereas negativity among users’ friends, or consonance of sentiments between users and friends had less impact on polarization. The findings are related to literatures on negativity bias, optimal distinctiveness theory, and intergroup contact theory. •Research on attitude polarization has typically focused on content or congeniality.•This paper looks at language sentiment as a precursor of polarization.•4 million tweets on two controversial topics were examined via sentiment analysis.•Negative sentiment of a person's own tweets increased polarization.•Negative sentiment of tweets from a person's friends reduced polarization.
ISSN:0747-5632
1873-7692
DOI:10.1016/j.chb.2020.106663