Who Drives the Diffusion of Incivility in Immigration Discussion? Exploring the Role of Morality and Issue Stance

This study examines incivility diffusion in immigration discourse networks from the lens of three incivility communicator types: amplifiers, copycats, and attenuators. This research draws on Moral Foundation Theory to analyze how users’ predispositions, as manifested in their moral expressions and immigration stance (anti-immigration or pro-immigration), help explain their roles in the diffusion of incivility on social media. A combination of human efforts, AI-assisted annotation, and a transformer-based language model was adopted to classify users’ immigration stance, morality expression, and incivility expression. Multinomial regression revealed that amplifiers score high on binding moral values, especially sanctity. Attenuators often hold pro-immigration stances. Findings have implications for platform design and intervention strategies targeting the relational dynamics of incivility diffusion

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
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article

Who Drives the Diffusion of Incivility in Immigration Discussion? Exploring the Role of Morality and Issue Stance

Yiqi Li, Aimei Yang
ScholarSpace (University of Hawaii at Manoa)
Hate Speech and Cyberbullying Detection
article

Who Drives the Diffusion of Incivility in Immigration Discussion? Exploring the Role of Morality and Issue Stance

Yiqi Li, Aimei Yang
article en

Abstract

This study examines incivility diffusion in immigration discourse networks from the lens of three incivility communicator types: amplifiers, copycats, and attenuators. This research draws on Moral Foundation Theory to analyze how users’ predispositions, as manifested in their moral expressions and immigration stance (anti-immigration or pro-immigration), help explain their roles in the diffusion of incivility on social media. A combination of human efforts, AI-assisted annotation, and a transformer-based language model was adopted to classify users’ immigration stance, morality expression, and incivility expression. Multinomial regression revealed that amplifiers score high on binding moral values, especially sanctity. Attenuators often hold pro-immigration stances. Findings have implications for platform design and intervention strategies targeting the relational dynamics of incivility diffusion

ScholarSpace (University of Hawaii at Manoa)
Reduced inequalities
Openalex Percentile: Top 18%
Hate Speech and Cyberbullying Detection
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