If it Bleeds, it Doesn’t Lead: Emotional Appeals and Engagement in Immigration and Election Conversations on Twitter

The use of emotional appeals in political conversations can be an effective strategy to trigger short-term engagement but also result in detrimental societal outcomes as alienation, polarization, and extremism. Whereas scholars examined different individual types of emotional appeals, this study proposes a multifaceted approach to understanding emotional appeals. It applies computational methods to extract tone, discrete emotions and topic modeling to extract topics later classified into appraisal types. Findings show that whereas users primarily posted content that was negative in tone, angry in emotion, and critical in appraisal, Twitter users engaged more with positive, joyful, supportive, or just neutral content.

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Publication Details

Journal
Journal of Broadcasting & Electronic Media
Published
2025-02-20
DOI
https://doi.org/10.1080/08838151.2025.2459948
Citations
2
Primary Topic
Social Media and Politics
Type
article
Field-Weighted Citation Impact
7.73
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article

If it Bleeds, it Doesn’t Lead: Emotional Appeals and Engagement in Immigration and Election Conversations on Twitter

Jeonghyun Janice Lee, Kyle Lorenzano, Itai Himelboim, Xiaohui Cao et al.
2 citations
Journal of Broadcasting & Electronic Media
Social Media and Politics
7.73
article

If it Bleeds, it Doesn’t Lead: Emotional Appeals and Engagement in Immigration and Election Conversations on Twitter

Jeonghyun Janice Lee, Kyle Lorenzano, Itai Himelboim, Xiaohui Cao, Porismita Borah
article en
2 citations

Abstract

The use of emotional appeals in political conversations can be an effective strategy to trigger short-term engagement but also result in detrimental societal outcomes as alienation, polarization, and extremism. Whereas scholars examined different individual types of emotional appeals, this study proposes a multifaceted approach to understanding emotional appeals. It applies computational methods to extract tone, discrete emotions and topic modeling to extract topics later classified into appraisal types. Findings show that whereas users primarily posted content that was negative in tone, angry in emotion, and critical in appraisal, Twitter users engaged more with positive, joyful, supportive, or just neutral content.

Journal of Broadcasting & Electronic MediaVol. 69(1-2)
Louisiana State University (US), University of West Georgia (US), Washington State University (US)
Reduced inequalities
Openalex Percentile: Top 5%
Social Media and Politics
7.73
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