The Not-So-Silent "Majority": an Automated Content Analysis of Anti-Government Online Communities

The United States is facing an ongoing threat of political violence due to widespread anti-government sentiment that has proliferated across social media platforms. Most saliently, these violent sentiments manifested in the January 6, 2021, attack on the US Capitol during the certification of the 20...

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Detalles Bibliográficos
Autor principal: Leap, Bethany (Autor)
Otros Autores: Becker, Michael H.
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2023
En: Perspectives on terrorism
Año: 2023, Volumen: 17, Número: 1, Páginas: 103-122
Acceso en línea: Volltext (kostenfrei)
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Verificar disponibilidad: HBZ Gateway
Palabras clave:
Social Media
Political Violence
Content Analysis
text analysis
computational social science
Descripción
Sumario:The United States is facing an ongoing threat of political violence due to widespread anti-government sentiment that has proliferated across social media platforms. Most saliently, these violent sentiments manifested in the January 6, 2021, attack on the US Capitol during the certification of the 2020 general election. This research extends prior work on the online mobilisation to offline violence by analysing the text of online discussions leading up to the January 6 attack. We focus this examination on two central questions. First, what are the key themes and topics discussed within and across two social media platforms? Second, how did these themes and topics change over time? Focusing on two far-right anti-government online communities, we explore how support for political violence, disinformation, and electoral outcomes emerge and change over time. Our findings provide insight into possible strategies to counteract misinformation and the temporal trajectory of escalating violent sentiment within and across online communities. Further, this study highlights the importance of collecting data prospectively and demonstrates the value of automated content analysis and text data in understanding anti-government extremist sentiments.
Notas:Literaturverzeichnis: Seite 119-122
Descripción Física:Diagramme
ISSN:2334-3745