Existing fact-finding models assume availability of structured data and accurate information extraction, that is not usually available as online data gets more unstructured. We propose a novel, content-based trust propagation framework 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 a model to compute the trustworthiness of news sources and articles. We show that the proposed model helps assess trustworthiness of sources better and that ranking news articles based on trustworthiness learned from the content-driven model is significantly better than baselines that ignore either the content quality or the trust framework.