Web sources often provide different and even conflicting information. Simple voting-based strategies have already shown limitations at identifying the correct answer to a user query with the presence of unreliable sources. In order to identify the correct answer, corroboration techniques have been proposed and proved to be effective for such tasks. In this paper, we investigate the corroboration problem in which most or all facts have only affirmative statements from sources. A fact is either true or false, and an affirmative statement from a source indicates its support for a fact being true. Unfortunately, state-of-the-art corroboration techniques rely on conflicting information to differentiate the trustworthiness of the sources and we demonstrate their limitations in our scenario. Different from existing techniques that consider a single trust score for each source, we propose a novel algorithm that utilizes a multi-value trust score toward different subsets of facts. By considering the information entropy of the unknown facts, our algorithm incrementally evaluates facts and updates the estimates on the trust scores for the sources. We conduct experiments using both synthetic and real-world datasets and demonstrate that our algorithm significantly outperforms existing approaches in precision and accuracy.