Abstract: Techniques are provided for rating the veracity of content distributed via digital communications sources by creating an ontology and selecting keywords for a topic of the content, creating a customizable intelligence channel for the topic, and extracting from the customizable intelligence channel a first list of potential experts on the topic sorted by at least relevance and influence. The list of experts may be supplemented by mining trusted media sources to extract a second list of potential experts or witnesses on the topic. The first and second lists of potential experts are evaluated as a function of at least one of professionalism, reliability, proximity, experience, responsiveness, and lack of self-interest in the topic to identify a short list of experts. The content is provided to the short list of experts, who are polled about the veracity of the content to create a veracity score for delivery with the content.

Abstract: Techniques are provided for rating the veracity of content distributed via digital communications sources by creating an ontology and selecting keywords for a topic of the content, creating a customizable intelligence channel for the topic, and extracting from the customizable intelligence channel a first list of potential experts on the topic sorted by at least relevance and influence. The list of experts may be supplemented by mining trusted media sources to extract a second list of potential experts or witnesses on the topic. The first and second lists of potential experts are evaluated as a function of at least one of professionalism, reliability, proximity, experience, responsiveness, and lack of self-interest in the topic to identify a short list of experts. The content is provided to the short list of experts, who are polled about the veracity of the content to create a veracity score for delivery with the content.