Open source intelligence (OSINT) is intelligence gathered from publicly available data sources such as academic publications, journals, social media sites, online communities, and newspapers, among others. OSINT was developed for espionage purposes during the Second World War and gained popularity in the modern era as a result of the internet revolution, which has resulted in the accumulation of massive amounts of data on the Internet. The integration of artificial intelligence with OSINT, as well as its role in cyber security are both issues of present day research which is in need of improved statistical foundations and contributions. Text mining, pattern matching, entity extraction, natural language processing, machine learning, distributed learning, complex networks, cloud computing, and big data have either made significant contributions to modern OSINT or are developing in its relation. Precision, reliability, promptness, and, most importantly, gaining a competitive advantage are the criteria for evaluating intelligence, including OSINT (Benes, 2013; Yogish Pai et al., 2021).
Motivated by the importance of the topic and the statistical issues it raises, the
RSS Discussion Meetings Committee, along with the RSS Sections on
Emerging Applications and Computational Statistics and Machine Learning, are jointly inviting submissions of discussion papers on open source intelligence from a statistical perspective. These papers can cover any methodological or applied aspect or theoretical underpinning on the topic. Examples of topics of interest include (but are not restricted to):
- Differential privacy and local differential privacy approaches
- Open source intelligence in the age of artificial intelligence
- Cybersecurity and open source intelligence
- Cloud computing and computational aspects of OSINT
- Statistical and mathematical properties of OSINT algorithms
- OSINT algorithms and machine learning
- Generalisation, fairness, unbalanced data, machine learning at scale and OSINT
- Statistical criteria to evaluate intelligence
- Open data practices and open source intelligence
- Novel applications in robotics and computer science, cryptography, climate change, environment and health among others.
Papers selected for publication will be presented at a multi-paper discussion meeting held at the RSS International Conference in Edinburgh, UK, in September 2025, and subsequently published in one of the series of
Journal of the Royal Statistical Society (JRSS), together with all contributions to the discussion at the meeting itself or submitted in writing shortly afterwards. All submitted papers are refereed, both for their scientific quality and their potential to generate discussion. Papers that meet the first criterion but not the second may, with the agreement of the authors, be referred to the editors of the Journal for review as a regular paper.
Submitted papers should be substantially shorter than is typical for a single‐paper discussion meeting (16 pages max. including references but excluding supplementary material, following standard JRSS formatting instructions). We shall employ a two-step process to expedite the peer review process.
Full details are as follows:
- Abstract submission. Authors are invited to send a single‐page abstract (400 words max) of their proposed paper to Judith Shorten, RSS Journals Manager (journal[at]rss.org.uk) by 16 July 2024.
- Full paper submission. Notification of accepted abstracts will be made by 31 July 2024 together with an invitation to submit a full paper. Full papers (16 pages max) should then be submitted via manuscript central to the most appropriate journal series (A, B or C) selecting the “Discussion Paper” option. The deadline for full paper submission is 30 November 2024.
- Refereeing. All papers received by 30 November 2024 will be refereed using the Society’s standard criteria for discussion meeting papers (scientific quality and potential to generate discussion).
- Final versions of accepted papers will be ready for pre‐printing by mid‐2025.
Informal enquiries about the call can be made by email to the Discussion Papers Editor, Adam Sykulski at adam.sykulski@imperial.ac.uk.