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11/16/2016 03:30 pm
CoRE B (305)

Extracting Users in Community Question-Answering in Particular Contexts

Long Le, Dept. of Computer Science

Defense Committee: Prof. Chirag Shah (Chair), Prof. Amélie Marian, Prof. Thu Nguyen, Prof. Panos Ipeirotis (NYU)


Community Question-Answering (CQA) services, such as Yahoo! Answer and Stack Overflow, have become important sources of seeking and sharing information. Online users use CQA to look for information and share knowledge on topics ranging from arts to travel. The questions posted on CQA sites are often relying on the wisdom of the crowd, that is, the best answer could come from a culmination of several answers by different people with varying expertise and opinions. Given that CQA is a user-driven service, user experience becomes an important aspect, affecting the activeness and even the survival of the site. In this work, I am interested in studying the behavior of the users who participate in CQA. Specifically, I wish to understand how different types of users could be identified based on their behaviors on a CQA-specific problem at hand. The behavior of a user depends on a particular context. For example, when we say that Alice is a ‘’good user’’, it depends on which context. She might be a good user in the whole community, a good user in a topic, a good user in a particular question, or a good user in a particular answer. In this dissertation, I will study and extract users in different levels of granularity. Examples of such classes of users include (i) potential answerers (ii) good/bad answerers, (iii) struggling users, and (iv) rising star users. The findings in this dissertation will be useful in identifying specifics type users in the CQA.