collaboration

Crowdsourced investigations shore up democratic institutions by debunking misinformation and uncovering human rights abuses. However, current crowdsourcing approaches rely on simplistic collaborative or competitive models and lack technological sup- port, limiting their collective impact. Prior research has shown that blending elements of competition and collaboration can lead to greater performance and creativity, but crowdsourced investiga- tions pose unique analytical and ethical challenges. In this paper, we employed a four-month-long Research through Design process to design and evaluate a novel interaction style called collabora- tive capture the flag competitions (CoCTFs). We instantiated this interaction style through CoSINT, a platform that enables a trained crowd to work with professional investigators to identify and inves- tigate social media misinformation. Our mixed-methods evaluation showed that CoSINT leverages the complementary strengths of competition and collaboration, allowing a crowd to quickly identify and debunk misinformation. We also highlight tensions between competition versus collaboration and discuss implications for the design of crowdsourced investigations.

Online investigations are increasingly conducted by individuals with diverse skill levels and experiences, with mixed results. Novice investigations often result in vigilantism or doxxing, while expert investigations have greater success rates and fewer mishaps. Many of these experts are involved in a community of practice known as Open Source Intelligence (OSINT), with an ethos and set of techniques for conducting investigations using only publicly available data. Through semi-structured interviews with 14 expert OSINT investigators from nine different organizations, we examine the social dynamics of this community, including the collaboration and competition patterns that underlie their investigations. We also describe investigators’ use of and challenges with existing OSINT tools, and implications for the design of social computing systems to better support crowdsourced investigations.