OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence Investigations
CoRR(2024)
摘要
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 the 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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