@inproceedings{da35859d6d624ddb80649041d5c62f00,
title = "Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack",
abstract = "Social media platforms have enabled extremists to organize violent events, such as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable professional investigators and amateur sleuths to collaboratively collect and identify imagery of suspects with the goal of holding them accountable for their actions. Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. Using topic modeling, we find that information sharing is the main focus of the community. We also note an increase in awareness of privacy concerns. Furthermore, using social network analysis, we show how some participants played important roles in the community. Finally, we discuss implications for the content and structure of online crowdsourced investigations.",
keywords = "Capitol Riot, collective action, crowdsourced investigations, crowdsourcing, extremism, social network analysis, topic modeling",
author = "Tianjiao Yu and Sukrit Venkatagiri and Ismini Lourentzou and Kurt Luther",
note = "We wish to thank Vikram Mohanty for initial data collection. This work was supported by NSF IIS-1651969.; 2023 World Wide Web Conference, WWW 2023 ; Conference date: 30-04-2023 Through 04-05-2023",
year = "2023",
month = apr,
day = "30",
doi = "10.1145/3543507.3583514",
language = "English (US)",
series = "ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023",
publisher = "Association for Computing Machinery",
pages = "3849--3858",
booktitle = "ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023",
address = "United States",
}