Identifying and predicting the desire to help in social question and answering

The increasing volume of questions posted on social question and answering sites has triggered the development of question routing services.  Most of these routing algorithms are able to recognize effectively individuals with the required knowledge to answer a specific question. However, just because people have the capability to answer a question, does not mean that they have the desire to help.  In this research, we evaluate the practical performance of the question routing services in social context by analyzing the knowledge sharing behavior of users in social Q&A process in terms of their participation, interests, and connectedness. We collect questions and answers over a ten-month period from Wenwo, a major Chinese question routing service. Using 340,658 questions and 1,754,280 replies, findings reveal separate roles for knowledge sharers and consumers. Based on this finding, we identify knowledge sharers from non-sharers a priori in order to increase the response probabilities. We evaluate our model based on an analysis of 3006 Wenwo knowledge sharers and non-sharers. Our experimental results demonstrate knowledge sharer prediction based solely on non-Q&A features achieves a 70% success rate in accurately identifying willing respondents.