One of the main challenges in intelligence work is to assess the trustworthiness of data sources. In an adversarial setting, in which the subjects under study actively try to disturb the data gathering process ([5]), trustworthiness is one of the most important properties of a source. The recent increase in usage of open source data has exacerbated the problem, due to the proliferation of sources. In this paper we propose computerized methods to help analysts evaluate the truthfulness of data sources (open or not). We apply methods developed in database and Semantic Web research to determine data quality (which includes truthfulness but also other related aspects like accuracy, timelines, etc.). Research on data quality has made frequent use of provenance metadata ([3]). This is metadata related to the origin of the data: where it comes from, how and when it was obtained, and any relevant conditions that might help determine how it came to be in its current form. We study the application of similar methods to the particular situation of the Intelligence analyst, focusing on trust ([2]).
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