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Coordinating Narratives Framework for cross-platform analysis in the 2021 US Capitol riots
Lynnette Hui Xian Ng1, Iain J Cruickshank1, Kathleen M Carley1
1CASOS, Institute for Software Research, Carnegie Mellon University, 4665 Forbes Avenue, Pittsburgh, 15213 PA USA.
This study introduces a framework to detect coordinated disinformation campaigns on social media, identifying networks of users posting similar content related to the 2020 U.S. election and the Capitol riots.
Area of Science:
- Computational Social Science
- Network Analysis
- Disinformation Studies
Background:
- Disinformation campaigns on social media can incite offline violence.
- Understanding coordinated messaging is crucial for combating harmful narratives.
Purpose of the Study:
- To develop a general methodology for uncovering coordinated messaging on social media platforms.
- To analyze user posts on Parler and Twitter to detect disinformation networks.
Main Methods:
- Introduction of the Coordinating Narratives Framework.
- Construction of user-to-user coordination, user-to-text, and text-to-text similarity graphs.
- Analysis of textual similarity between posts on Parler and Twitter.
Main Results:
- Detection of coordinated user clusters posting similar disinformation narratives.
- Identification of networks supporting disinformation related to the U.S. 2020 elections and the Capitol riots.
- Extension of the methodology to identify similar messaging on Twitter.
Conclusions:
- The Coordinating Narratives Framework effectively identifies coordinated disinformation.
- The study highlights the role of social media in spreading election-related disinformation.
- The methodology can be applied to various platforms for disinformation analysis.
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