ArguMentor: Augmenting User Experiences with Counter-Perspectives

Opinion pieces often represent only one side of any story, which can influence users and make them susceptible to confirmation bias and echo chambers in society. Moreover, humans are also bad at reading long articles -- often indulging in idle reading and re-reading. To solve this, we design ArguMentor, an end-to-end system that highlights claims in opinion pieces, generates counter-arguments for them using an LLM, and generates a context-based summary of the passage based on current events. It further enhances user interaction and understanding through additional features like Q&A bot, DebateMe and highlighting trigger windows. Our survey and results show that users can generate more counterarguments and on an average have more neutralized views after engaging with the system.

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.