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...
| Authors: | ; |
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2023
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| In: |
Perspectives on terrorism
Year: 2023, Volume: 17, Issue: 1, Pages: 103-122 |
| Online Access: |
Volltext (kostenfrei) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
MARC
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| 245 | 1 | 4 | |a The Not-So-Silent "Majority" |b an Automated Content Analysis of Anti-Government Online Communities |c Bethany Leap, Michael H. Becker |
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| 520 | |a 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. | ||
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| 650 | 4 | |a computational social science | |
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