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Proceedings Paper

Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT
Author(s): Georgiy Levchuk; Charlotte Shabarekh
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Paper 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.

Paper Details

Date Published: 3 May 2017
PDF: 19 pages
Proc. SPIE 10207, Next-Generation Analyst V, 1020704 (3 May 2017); doi: 10.1117/12.2263546
Show Author Affiliations
Georgiy Levchuk, Aptima, Inc. (United States)
Charlotte Shabarekh, Aptima, Inc. (United States)


Published in SPIE Proceedings Vol. 10207:
Next-Generation Analyst V
Timothy P. Hanratty; James Llinas, Editor(s)

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