Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT

Georgiy Levchuk; Charlotte Shabarekh

doi:10.1117/12.2263546

3 May 2017 Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT

Georgiy Levchuk, Charlotte Shabarekh

Proceedings Volume 10207, Next-Generation Analyst V; 1020704 (2017) https://doi.org/10.1117/12.2263546
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Conference Presentation

Citation Download Citation

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 (3 May 2017); https://doi.org/10.1117/12.2263546

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