Social Q&A sites have been emerging as a platform for people to seek information and social supports around health topics. Identifying users’ information needs from the questions can significantly help social Q&A sites serve their users better. Prior research had attempted to understand askers’ intentions and implicit needs by classifying hidden intent from questions, while the non-trivial categorization was only able to be conducted with a limited size of data. In this study, we aim to develop a scalable categorization method that can categorize the askers’ intent in a large set of health-related questions via crowdsourcing. We conducted a preliminary experiment on Amazon Mechanical Turk to evaluate our categorization method. Our results suggests both challenges and opportunities for understanding health information intent via crowdsourcing.ye
