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.
Georgiy Levchuk and Charlotte Shabarekh, "Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT," Proc. SPIE 10207, Next-Generation Analyst V, 1020704 (Presented at SPIE Defense + Security: April 10, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263546.
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