Temporal and semantic analysis of richly typed social networks from user-generated content sites on the Web

Zide Meng 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : We propose an approach to detect topics, overlapping communities of interest, expertise, trends and activities in user-generated content sites and in particular in question-answering forums such as StackOverFlow. We first describe QASM (Question & Answer Social Media), a system based on social network analysis to manage the two main resources in question-answering sites: users and contents. We also introduce the QASM vocabulary used to formalize both the level of interest and the expertise of users on topics. We then propose an efficient approach to detect communities of interest. It relies on another method to enrich questions with a more general tag when needed. We compared three detection methods on a dataset extracted from the popular Q&A site StackOverflow. Our method based on topic modeling and user membership assignment is shown to be much simpler and faster while preserving the quality of the detection. We then propose an additional method to automatically generate a label for a detected topic by analyzing the meaning and links of its bag of words. We conduct a user study to compare different algorithms to choose the label. Finally we extend our probabilistic graphical model to jointly model topics, expertise, activities and trends. We performed experiments with real-world data to confirm the effectiveness of our joint model, studying the users’ behaviors and topics dynamics.
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Contributor : Zide Meng <>
Submitted on : Sunday, December 4, 2016 - 8:40:13 PM
Last modification on : Monday, November 5, 2018 - 3:52:09 PM
Long-term archiving on : Tuesday, March 21, 2017 - 6:20:00 AM


  • HAL Id : tel-01402612, version 1


Zide Meng. Temporal and semantic analysis of richly typed social networks from user-generated content sites on the Web. Computer Science [cs]. Université Nice Sophia Antipolis [UNS], 2016. English. ⟨tel-01402612v1⟩



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