Content-driven trust propagation framework

Existing fact-finding models assume availability of structured data or accurate information extraction. However, as online data gets more unstructured, these assumptions are no longer valid. To overcome this, we propose a novel, content-based, trust propagation framework that relies on signals from the textual content to ascertain veracity of free-text claims and compute trustworthiness of their sources. We incorporate the quality of relevant content into the framework and present an iterative algorithm for propagation of trust scores. We show that existing fact finders on structured data can be modeled as specific instances of this framework. Using a retrieval-based approach to find relevant articles, we instantiate the framework to compute trustworthiness of news sources and articles. We show that the proposed framework helps ascertain trustworthiness of sources better. We also show that ranking news articles based on trustworthiness learned from the content-driven framework is significantly better than baselines that ignore either the content quality or the trust framework.