Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Are Bushmeat Hunters Profit Maximizers or Simply Brigands of Opportunity?
Bushmeat hunters on Bioko Island, Equatorial Guinea use shotguns and snares to capture wild arboreal and ground animals for sale in the Malabo Bushmeat market. Two tools for the analysis of economic efficiency, the produ…
Exploration and Coordination of Complementary Multi-Robot Teams in a Hunter and Gatherer Scenario
The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one ag…
Task PlanningLetting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
Identifying and assigning keywords at scale is a technical, practical, and ethical challenge for crowdsourced collections. This article reports the findings of the "Extracting Keywords from Crowdsourced Collections" proj…
Keyword ExtractionGenerating Fake Cyber Threat Intelligence Using Transformer-Based Models
Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be gene…
Data PoisoningKnowledge GraphsLanguage ModellingSentenceText-to-hashtag Generation using Seq2seq Learning
In this paper, we studied whether models based on BiLSTM and BERT can predict hashtags in Brazilian Portuguese for Ecommerce websites. Hashtags have a sizable financial impact on Ecommerce. We processed a corpus of Ecomm…