Knowledge-based communities are popular Web-based tools that allow members to share and seek knowledge globally. However, research on how to search effectively within such knowledge repositories is scant. In this paper we study the problem of finding authoritative documents for user queries within a knowledge-based community. Unlike prior research on the ranking function design which considers only content or hyperlink information, we leverage the social network information embedded in the rich social media, in addition to content, to design novel ranking strategies. Using the Knowledge Adoption Model as the guiding theoretical framework, we design features that gauge the two major factors affecting users’ knowledge adoption decisions: argument quality (AQ) and source credibility (SC). We design two ranking strategies that blend these two sources of evidence with the content-based relevance judgment. A preliminary study using a real world knowledge-based community showed that both AQ and SC features improved search effectiveness.