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OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence Investigations

Abstract

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.

Key words: Human-centered computing; Collaborative and social computing systems and tools; Empirical studies in HCI; OSINT; open source intelligence; design-based research; complex sensemaking; collaboration; crowdsourcing

Authors

 

  • Publication/Conference: 2024 ACM Conference on Computer-Supported Cooperative Work and Social Computing
  • Date: Nov. 9-13, 2024
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