Echoes of Toxicity: The Spiral of Toxicity in Twitter Immigration Discourse During the Biden Era

Drawing from the conspiracy spiral theory, this research explores how the toxic immigration issue debates on Twitter escalated. Immigration has generated controversial and often hostile discussion on social media. Using a longitudinal Twitter immigration-related dataset that covers more than three years of Biden’s administration, this research explores how toxicity level between pro-, neutral, and anti-immigration publics evolves and diffuses. Toxicity is conceptualized as a symptom reflecting communication conflicts exacerbated by the clustering of echo chambers and social media algorithms. Employing large language models and machine learning to model influence dynamics of Twitter mention networks, findings inform how toxicity travels and evolves within and between network clusters. This research deepens the understanding of how public conflict evolves in polarized issue publics.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

Echoes of Toxicity: The Spiral of Toxicity in Twitter Immigration Discourse During the Biden Era

Aimei Yang, Yiqi Li
ScholarSpace (University of Hawaii at Manoa)
Misinformation and Its Impacts
article

Echoes of Toxicity: The Spiral of Toxicity in Twitter Immigration Discourse During the Biden Era

Aimei Yang, Yiqi Li
article en

Abstract

Drawing from the conspiracy spiral theory, this research explores how the toxic immigration issue debates on Twitter escalated. Immigration has generated controversial and often hostile discussion on social media. Using a longitudinal Twitter immigration-related dataset that covers more than three years of Biden’s administration, this research explores how toxicity level between pro-, neutral, and anti-immigration publics evolves and diffuses. Toxicity is conceptualized as a symptom reflecting communication conflicts exacerbated by the clustering of echo chambers and social media algorithms. Employing large language models and machine learning to model influence dynamics of Twitter mention networks, findings inform how toxicity travels and evolves within and between network clusters. This research deepens the understanding of how public conflict evolves in polarized issue publics.

ScholarSpace (University of Hawaii at Manoa)
Peace, Justice and strong institutions
Openalex Percentile: Top 23%
Misinformation and Its Impacts
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