Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites

摘要

Community Question Answering (CQA) websites such as Quora are widely used for users to get high quality answers. Users are the most important resource for CQA services, and the awareness of user expertise at early stage is critical to improve user experience and reduce churn rate. However, due to the lack of engagement, it is difficult to infer the expertise levels of newcomers. Despite that newcomers expose little expertise evidence in CQA services, they might have left footprints on external social media websites. Social login is a technical mechanism to unify multiple social identities on different sites corresponding to a single person entity. We utilize the social login as a bridge and leverage social media knowledge for improving user performance prediction in CQA services. In this paper, we construct a dataset of 20,742 users who have been linked across Zhihu (similar to Quora) and Sina Weibo. We perform extensive experiments including hypothesis test and real task evaluation. The results of hypothesis test indicate that both prestige and relevance knowledge on Weibo are correlated with user performance in Zhihu. The evaluation results suggest that the social media knowledge largely improves the performance when the available training data is not sufficient.