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.

翻译：开源情报（OSINT）调查完全依赖社交媒体等公开可用数据，在解决犯罪案件和追究政府责任方面发挥着日益重要的作用。然而，数据量的持续增长与任务的复杂性，意味着迫切需要扩大并加速开源情报调查的规模。专家主导的众包方法虽展现出潜力，但往往局限于狭窄的任务或领域，或需要专家调查者与众人之间建立资源密集型的长期合作关系。为弥补这一不足，我们提出了一种灵活框架，使跨领域调查者能够借助众包力量支持开源情报的发现与验证。我们采用基于设计的研究（DBR）方法，开发了开源情报研究工作室（ORS）这一社会技术系统，通过该平台训练新手众包人员支持专业调查者完成复杂的开源情报调查。通过定性评估，我们发现ORS能够促进跨多个领域的道德且高效的开源情报调查。我们还讨论了专家-众包协作的更广泛启示及未来工作方向。