The number of unanswered questions on Community based Question Answering (CQA) websites has increased significantly due to the rising number of duplicate questions. This is a serious problem, one that could lead to the decline of such beneficial websites. This thesis presents novel avenues that use gaze tracking technology and behavioral testing to tackle this problem. Based on prior studies on web search behaviors, we assumed that adding contextual information (snippets) to proposed related questions displayed on the `Ask a Question' page of the CQA website iFixit would improve the asker experience and reduce their tendency to post a new duplicate question. The first lab experiment where this web page was redesigned and compared to the original one was conducted on 8 participants. Results confirmed that participants were more likely to find an answer to their question on the redesigned page. A second experiment, conducted remotely and on a larger sample of 74 participants, aimed to discover strategic attributes that increase the perceived similarity of question pairs. These attributes were used in the third lab experiment (20 participants) to redesign and assess the snippets from Experiment 1. Results indicated that snippets containing `symptom(s)' and `cause(s)' attributes constitute an incremental improvement over basic snippets: they are perceived as slightly more relevant and require significantly less gaze fixations on the asker's part.

Community-based Question Answering (CQA) services enable members to ask questions and have them answered by the community. These services have the potential of rapidly creating large archives of questions and answers. However, their information is rarely exploited. This thesis presents a new statistical topic model for modeling Question-Answering archives. The model explicitly captures topic dependency and correlation between questions and answers, and models differences in their vocabulary. The proposed model is applied for the task of Question Answering and its performance is evaluated using a dataset extracted from the programming website Stack Overflow. Experimental results show that it achieves improved performance in retrieving the correct answer for a query question compared to the LDA model. The model has also been applied for Automatic Tagging and comparisons with LDA show that the new model achieves better clustering performance for larger numbers of topics.