Abstract: The rise of Knowledgcsharing platform on the Internet in China provides a new approach for Automatic Question Answering. However, the quality of User-Generated Content in such social networks may vary significantly,from useless information to malice spam. Identifying and filtering such content arc particularly important to improve users' experience and the performance of Question Answering System. We first extracted a set of question answer content from Chinese Community Question Answering site, investigated a series of statistic characteristics on the interaction of participants, and then manually annotated quality of a subset of these questions and answers. By combining text features and non-text features provided by the community extracted from those questions and answers,we established acontent quality classification model for evaluation and prediction. We find that this model is able to distinguish highquality ones from others with considerable accuracy.

Key words: Community question answering, Social networks, Machine learning, Question and answer quality evaluation and prediction, Human annotation