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Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-paced settings: (1) navigating the unique affordances of VSPs, (2) understanding VSP content and determining its authenticity, and (3) novel user behaviors on VSPs for spreading misinformation. By highlighting these challenges, we hope that researchers can reflect on how to adapt existing research methods and tools to these new contexts, or develop entirely new ones.

Online extremism can quickly spill over into the physical world and have dangerous consequences, as when rioters attacked the U.S. Capitol on January 6, 2021. While information and communication tech- nologies have enabled extremists to plan and organize violent events, they have also enabled collective action by others to identify the perpetrators and hold them accountable. Through a mixed-methods case study of Sedition Hunters, a Twitter-based community whose goal is to identify individuals who took part in the Capitol attack, we explore: 1) how the community formed and changed over time; 2) the motives, ethos, and roles of its members; and 3) the methods and software tools they used to identify individuals and coordinate their activities.

Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. Here, we discuss a real-world deployment of a system we built, GroundTruth, that enables experts to direct crowds in determining the exact geographic location where an image was taken, a key step in verifying visual evidence. We discuss the challenges working with these experts and the methods we have employed to study our software deployment.

Misinformation poses a threat to public health, safety, and democracy. Training novices to debunk visual misinformation with image verification techniques has shown promise, yet little is known about how novices do so in the wild, and what methods prove effective. Thus, we studied 225 verification challenges posted by experts on Twitter over one year with the aim of improving novices’ skills. We collected, annotated, and analyzed these challenges and over 3,100 replies by 304 unique participants. We find that novices employ multiple tools and approaches, and techniques like collaboration and reverse image search significantly improve performance.

Crowdsourced labeling of political social media content is an area of increasing interest, due to the contextual nature of political content. However, there are substantial risks of human biases causing data to be labelled incorrectly, possibly advantaging certain political groups over others. Inspired by the social computing theory of social translucence and findings from social psychology, we built PairWise, a system designed to facilitate interpersonal accountability and help mitigate biases in political content labelling.