3 May 2017 Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT
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Abstract
Today’s battlefields are shifting to “denied areas”, where the use of U.S. Military air and ground assets is limited. To succeed, the U.S. intelligence analysts increasingly rely on available open-source intelligence (OSINT) which is fraught with inconsistencies, biased reporting and fake news. Analysts need automated tools for retrieval of information from OSINT sources, and these solutions must identify and resolve conflicting and deceptive information. In this paper, we present a misinformation detection model (MDM) which converts text to attributed knowledge graphs and runs graph-based analytics to identify misinformation. At the core of our solution is identification of knowledge conflicts in the fused multi-source knowledge graph, and semi-supervised learning to compute locally consistent reliability and credibility scores for the documents and sources, respectively. We present validation of proposed method using an open source dataset constructed from the online investigations of MH17 downing in Eastern Ukraine.
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Georgiy Levchuk, Georgiy Levchuk, Charlotte Shabarekh, Charlotte Shabarekh, } "Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT", Proc. SPIE 10207, Next-Generation Analyst V, 1020704 (3 May 2017); doi: 10.1117/12.2263546; https://doi.org/10.1117/12.2263546
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