Fraud continues to proliferate online, from phishing and ransomware to impersonation scams. Yet automated prevention approaches adapt slowly and may not reliably protect users from falling prey to new scams. To better combat online scams, we developed ScamPilot, a conversational interface that inoculates users against scams through simulation, dynamic interaction, and real-time feedback. ScamPilot simulates scams with two large language model-powered agents: a scammer and a target. Users must help the target defend against the scammer by providing real-time advice. Through a between-subjects study (N=150) with one control and three experimental conditions, we find that blending advice-giving with multiple choice questions significantly increased scam recognition (+8%) without decreasing wariness towards legitimate conversations. Users' response efficacy and change in self-efficacy was also 9% and 19% higher, respectively. Qualitatively, we find that users more frequently provided action-oriented advice over urging caution or providing emotional support. Overall, ScamPilot demonstrates the potential for inter-agent conversational user interfaces to augment learning.

Public records laws are standard for state and federal governments across the U.S. Such laws rest on the core notion that transparency, enacted through access to governmental documentation, will ensure accountability and minimize governmental corruption. However, these laws can also be exploited to harass or burden government employees, including researchers at public universities. CSCW researchers have both an acute vulnerability to these requests, e.g., due to increasing politicization of topics core to our research agenda, and a unique opportunity to study challenges around public records requests as they intersect with the design and use of collaboration technologies. In this paper, we explore how a large number of public records requests (PRRs) affected the collaborative work of a large, multi-site academic research project. We find that — though participants believed PRRs were a valuable tool for government transparency — they added a complicated new dimension to distributed "work" which blends personal and professional dimensions. We explain how researchers interpreted these requests and adapted their communication techniques in response. We discuss how current technologies for communication and collaboration are unprepared to ensure personal privacy and security within adversarial research environments — and collaborative work environments more broadly. Finally, we highlight the misalignment between long-standing transparency laws and the current design of collaboration technologies and provide recommendations for updating these laws.

Generative AI has the potential to augment artists’ creative expression, while simultaneously harming their professions through unethical data collection practices and replacement of human labor. We conducted a thematic analysis of social media posts to understand artists’ perceptions and experiences of the direct and indirect impact of generative AI on their profession. Our findings also highlight growing public distrust toward artists amidst the rise of generative AI, with accusations of using AI tools leading to stress and fear of unemployment. Our study provides valuable insights into the complex interplay between artists, generative AI, and the public. We discuss potential protective measures for artists, including regulatory interventions and opt-in/out data collection, and explore future impacts of generative AI on artists’ creative processes.

Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex nature of tasks, however, means there is a pressing need to scale and speed up OSINT investigations. Expert-led crowdsourcing approaches show promise, but tend to either focus on narrow tasks or domains, or require resource-intense, long-term relationships between expert investigators and crowds. We address this gap by providing a flexible framework that enables investigators across domains to enlist crowdsourced support for discovery and verification of OSINT. We use a design-based research (DBR) approach to develop OSINT Research Studios (ORS), a sociotechnical system in which novice crowds are trained to support professional investigators with complex OSINT investigations. Through our qualitative evaluation, we found that ORS facilitates ethical and effective OSINT investigations across multiple domains. We also discuss broader implications of expert-crowd collaboration and opportunities for future work.

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 support, limiting their collective impact. Prior research has shown that blending elements of competition and collaboration can lead to greater performance and creativity, but crowdsourced investigations 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 collaborative 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 investigate 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.

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.

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.

An estimated 100,000 people work today as commercial content moderators. These moderators are often exposed to disturbing content, which can lead to lasting psychological and emotional distress. This literature review investigates moderators’ psychological symptomatology, drawing on other occupations involving trauma exposure to further guide understanding of both symptoms and support mechanisms. We then introduce wellness interventions and review both programmatic and technological approaches to improving wellness. Additionally, we review methods for evaluating intervention efficacy. Finally, we recommend best practices and important directions for future research. Content Warning: we discuss the intense labor and psychological effects of CCM, including graphic descriptions of mental distress and illness.

Investigators in fields such as journalism and law enforcement have long sought the public's help with investigations. New technologies have also allowed amateur sleuths to lead their own crowdsourced investigations - that have traditionally only been the purview of expert investigators - with mixed results. Through an ethnographic study of a four-day, co-located event with over 250 attendees, we examine the human infrastructure responsible for enabling the success of an expert-led crowdsourced investigation. We find that the experts enabled attendees to generate useful leads; the attendees formed a community around the event; and the victims' families felt supported. Additionally, the co-located setting, legal structures, and emergent social norms impacted collaborative work practice. We also surface three important tensions to consider in future investigations and provide design recommendations to manage these tensions.

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. In this paper, we introduce the concept of shared representations for crowd--augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations-a diagram, grid, and heatmap-to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert-crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond.