Effective and Efficient Approaches to Retrieving and Using Expertise in Social Media

The recent popularity of social media is changing the way people share and acquire knowledge. Companies started using intra-organizational social media applications in order to improve the communication and collaboration among employees. In addition to their professional use, people have been using these sites in their personal lives for information acquisition purposes, such as community question answering sites for their questions. In such environments the interactions do not always occur between users who know each other well enough to assess expertise of one another or trust the accuracy of their created content. This dissertation addresses this problem by estimating topic specific expertise scores of users which can be also used to improve the expertise related applications in social media. 

Expert retrieval has been widely studied using organizational documents; however, the additional structure and information available in social media provide the opportunity to improve the developed expert finding approaches. One such difference is the availability of different types of user created content, which can be used to represent users’ expertise and the information need being searched more effectively in order to retrieve an initial set of good expert candidates. The underlying social network structure constructed from the interactions among the users, such as commenting or replying, is also investigated and topic-specific authority graph construction and estimation approaches are developed in order to estimate topic-specific authorities from these graphs. Finally, the available timestamp information within social media is explored and a more dynamic expert identification approach which takes into account the recent topic-specific interest of users as well as their availability is proposed. 

This available information is explored and the proposed approaches are combined in an expert identification system which consists of three parts; (1) content-based retrieval, (2) authority estimation and (3) temporal modeling. Depending on the environment and task being tested, some or all of these parts can be used to identify topic-specific experts. This proposed system is applied to two data collections, an intra-organizational blog data and a popular community question answering site’s data, for three expertise estimation related tasks: identification of topic-specific expert bloggers, routing questions to users who can provide accurate and timely replies, and ranking replies based on responders’ question specific expertise. Statistically significant improvements are observed in all three tasks. In addition to improving the effectiveness of expert identification applications in social media, the proposed approaches are also more efficient which makes the proposed expert finding system applicable to real time environments.