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. / Doctor of Philosophy / Expert investigators leverage their advanced skills and deep experience to solve complex investigations, but they face limits on their time and attention. In contrast, there is growing interest among non-professional members of the public to participate in investigations, but they lack the expertise or may engage in harmful behavior. In this dissertation, I introduce a new concept called, expert-led crowdsourcing, that allows professionals and non-professionals to work together on a high-stakes investigations in two domains: journalism and law enforcement. First, I explored how expert-led crowdsourcing played out in CrowdSolve, a real-world investigation of two decades-old murder cases. At CrowdSolve, over 250 amateur sleuths supported eight law enforcement experts to uncover new leads two for the two cases. Second, I build two software applications, GroundTruth and CuriOSINTy, to better support expert-led crowdsourced investigations. GroundTruth helps investigators work with a crowd to find the exact geographic location where a photo was taken. CuriOSINTy extends GroundTruth's capabilities to help investigators with more complex and multiple investigative tasks involved in identifying and debunking misinformation on social media. Third, I compared and contrasted the three prior studies to develop a more detailed understanding of expert-led crowdsourced investigations 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 used in other professions.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/112963 |
| Date | 20 December 2022 |
| Creators | Venkatagiri, Sukrit |
| Contributors | Computer Science and Applications, Luther, Kurt, Mitra, Tanushree, North, Christopher L., Starbird, Kate, Rho, Ha Rim |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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