Finding Experts in Social Media Data using a Hybrid Approach
This addresses expert identification for users of social media platforms, but appears incremental as it integrates existing approaches.
The paper tackled the problem of expert finding in social media by developing a hybrid approach combining content analysis, social graph analysis, and Semantic Web technologies, resulting in a functional prototype called ExpertQuest that was evaluated for practicality.
Several approaches to the problem of expert finding have emerged in computer science research. In this work, three of these approaches - content analysis, social graph analysis and the use of Semantic Web technologies are examined. An integrated set of system requirements is then developed that uses all three approaches in one hybrid approach. To show the practicality of this hybrid approach, a usable prototype expert finding system called ExpertQuest is developed using a modern functional programming language (Clojure) to query social media data and Linked Data. This system is evaluated and discussed. Finally, a discussion and conclusions are presented which describe the benefits and shortcomings of the hybrid approach and the technologies used in this work.