This study examines the factors that affect the selection of the best answer in a Community-driven Question Answering service (Yahoo! Answers). Factors identified were classified into three categories namely, social, textual and content-appraisal features. Social features refer to the community aspects of the users involved and are extracted from the explicit user interaction and feedback. Textual features refer to the surface aspects of the text such as answer length, number of unique words etc. The content-appraisal features emphasis on the quality of the content and the relevance judgment used by the asker to select the best answer. The framework built comprises 12 features from the three categories. Based on a randomly selected dataset of 800 question-answer pairs from Yahoo!Answers, social, textual and content-appraisal features were collected. The results of logistic regression showed the significance of content-appraisal features over social and textual features. The implications of these findings for system development and for future research are discussed.