In many questions in Community Question Answering sites users look for the advice or opinion of other users who might oer diverse perspectives on a topic at hand. The novel task we address is providing supportive evidence for human answers to such questions, which will potentially help the asker in choosing answers that t her needs.We present a support retrieval model that ranks sentences from Wikipedia by their presumed support for a human answer. The model outperforms a state-of-the-art textual entailment system designed to infer factual claims from texts. An important aspect of the model is the integration of relevance oriented and support oriented features.