Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process.

The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline.

Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.

• Presents a coherent set of methods and processes for automating OSINT
• Focuses on algorithms and applications allowing the practitioner to get up and running quickly
• Includes fully developed case studies on the digital underground and predicting crime through OSINT

• Discusses the ethical considerations when using publicly available online data

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• 本书对从业人员和学术研究人员都有好处，讨论了应用方面的最新进展，提供了一套连贯的OSINT自动化方法和流程，并从跨学科的角度讨论了每个学科中发现的关键问题。
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• 编者罗伯特-莱顿、保罗-沃特斯和杰出的撰稿人利用多年的实践经验和大量的实例，讨论了OSINT的证据积累策略、社交媒体中的命名实体解析、分析社交媒体运动以估计群体规模、OSINT中的调查和定性技术，以及开放数据的地理空间推理。
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• 提出了一套连贯的OSINT自动化的方法和流程
• 专注于算法和应用，使从业者能够快速启动和运行
• 包括关于数字地下和通过OSINT预测犯罪的全面案例研究
• 讨论了使用公开可用的在线数据时的道德考虑因素