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Towards Automatic Veracity Assessment of Open Source Information
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2015 (English)In: 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015, 199-206 p.Conference paper (Refereed)
Abstract [en]

Intelligence analysis is dependent on veracity assessment of Open Source Information (OSINF) which includes assessment of the reliability of sources and credibility of information. Traditionally, OSINF veracity assessment is done by intelligence analysts manually, but the large volumes, high velocity, and variety make it infeasible to continue doing so, and calls for automation. Based on meetings, interviews and questionnaires with military personnel, analysis of related work and state of the art, we identify the challenges and propose an approach and a corresponding framework for automated veracity assessment of OSINF. The framework provides a basis for new tools which will give the intelligence analysts the ability to automatically or semi-automatically assess veracity of larger amounts of data in a shorter amount of time. Instead of spending their time working with irrelevant, ambiguous, contradicting, biased, or plain wrong data, they can spend more time on analysis.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 199-206 p.
Keyword [en]
Big Data, public domain software, Big Data, OSINF, automatic data veracity assessment, intelligence analysis, open source information, Automation, Big data, Interviews, Probabilistic logic, Reliability, Semantics, Twitter, NATO STANAG 2511, OSINF, big data, data veracity, reliability and credibility, trust, veracity assessment
National Category
Computer Science
URN: urn:nbn:se:kth:diva-179402DOI: 10.1109/BigDataCongress.2015.36ISI: 000380443700026ScopusID: 2-s2.0-84959501022ISBN: 978-1-4673-7277-0OAI: diva2:882888
Big Data (BigData Congress), 2015 IEEE International Congress on, New York, USA, June 27 - July 2, 2015.

QC 20151217

Available from: 2015-12-16 Created: 2015-12-16 Last updated: 2016-09-05Bibliographically approved

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