Browsing by Author "Venkatagiri, Sukrit"

Now showing 1 - 7 of 7
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    Compete, Collaborate, Investigate: Exploring the Social Structures of Professional OSINT Investigations
    Belghith, Yasmine; Venkatagiri, Sukrit; Luther, Kurt (ACM, 2022-04-27)
    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.
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    CoSINT: Designing a Collaborative Capture the Flag Competition to Investigate Misinformation
    Venkatagiri, Sukrit; Mukhopadhyay, Anirban; Hicks, David; Brantly, Aaron F.; Luther, Kurt (ACM, 2023-07-10)
    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 fag 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.
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    Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation
    Thebault-Spieker, Jacob; Venkatagiri, Sukrit; Mine, Naomi; Luther, Kurt (ACM, 2023-06-12)
    In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topics, fact-checking political claims, and enabling users to hide political content altogether. However, implementing these policies requires human judgement to label political content, and it is unclear how well human labelers perform at this task, or whether biases affect this process. Therefore, in this study we experimentally evaluate the feasibility and practicality of using crowd workers to identify political content, and we uncover biases that make it difficult to identify this content. Our results problematize crowds composed of seemingly interchangeable workers, and provide preliminary evidence that aggregating judgements from heterogeneous workers may help mitigate political biases. In light of these findings, we identify strategies to achieving fairer labeling outcomes, while also better supporting crowd workers at this task and potentially mitigating biases.
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    OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence Investigations
    Mukhopadhyay, Anirban; Venkatagiri, Sukrit; Luther, Kurt (ACM, 2024-04-23)
    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.
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    Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack
    Yu, Tianjiao; Venkatagiri, Sukrit; Lourentzou, Ismini; Luther, Kurt (ACM, 2023-04-30)
    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.
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    Supporting and Transforming High-Stakes Investigations with Expert-Led Crowdsourcing
    Venkatagiri, Sukrit (Virginia Tech, 2022-12-20)
    Expert investigators leverage their advanced skills and deep experience to solve complex investigations, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise and may engage in vigilante behavior. In this dissertation, I introduce and evaluate the framework of expert-led crowdsourcing through three studies across two domains, journalism and law enforcement. First, through an ethnographic study of two law enforcement murder investigations, I uncover tensions in a real-world crowdsourced investigation and introduce the expert-led crowdsourcing framework. Second, I instantiate expert-led crowdsourcing in two collaboration systems: GroundTruth and CuriOSINTy. GroundTruth is focused on one specific investigative task, image geolocation. CuriOSINTy expands the flexibility and scope of expert-led crowdsourcing to handle more complex and multiple investigative tasks: identifying and debunking misinformation. Third, I introduce a framework for understanding how expert-led crowdsourced investigations work and how to better support them. Finally, I conclude with a discussion of how expert-led crowdsourcing enables experts and crowds to do more than either could alone, as well as how it can be generalized to other domains.
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    Supporting High-Stakes Investigations with Expert-Led Crowdsourcing
    Venkatagiri, Sukrit (ACM, 2023-01-08)
    My dissertation introduces the concept of expert-led crowdsourcing (ELC) to support expert investigators who increasingly face limits on their time and attention. ELC combines experts’ domain knowledge and experience with the speed and scale of crowds. I study ELC in two investigative domains: journalism and law enforcement. Through four studies, I show: 1) how novice crowds can effectively augment expert investigators’ work practice; 2) the ethical tensions in conducting an ELC investigation for real-world, sensitive investigations; 3) how capture-the-flag competitions increase inter-team collaboration in ELC investigations; and 4) how different teamwork structures affect intra-team collaboration in ELC investigations.