Conference proceeding
Following the Social Media: Aspect Evolution of Online Discussion
SOCIAL COMPUTING, BEHAVIORAL-CULTURAL MODELING AND PREDICTION, v 6589
01 Jan 2011
Abstract
Due to the advance of Internet and Web 2.0 technologies, it is easy to extract thousands of threads about a topic of interest from an online forum but it is nontrivial to capture the blueprint of different aspects (i.e., subtopic. or facet) associated with the topic. To better understand and analyze a forum discussion given topic, it is important to uncover the evolution relationships (temporal dependencies) between different topic aspects (i.e. how the discussion topic is evolving). Traditional Topic Detection and Tracking (TDT) techniques usually organize topics as a flat structure but it does not present the evolution relationships between topic aspects. In addition, the properties of short and sparse messages make the content-based TDT techniques difficult to perform well in identifying evolution relationships. The contributions in this paper are two-folded. We formally define a topic aspect evolution graph modeling framework and propose to utilize social network information, content similarity and temporal proximity to model evolution relationships between topic aspects. The experimental results showed that, by incorporating social network information, our technique significantly outperformed content-based technique, in the task of extracting evolution relationships between topic aspects.
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Details
- Title
- Following the Social Media: Aspect Evolution of Online Discussion
- Creators
- Xuning Tang - Drexel UniversityChristopher C. Yang - Drexel University
- Contributors
- J Salerno (Editor)S J Yang (Editor)D Nau (Editor)S K Chai (Editor)
- Publication Details
- SOCIAL COMPUTING, BEHAVIORAL-CULTURAL MODELING AND PREDICTION, v 6589
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000297039300041
- Scopus ID
- 2-s2.0-79952426411
- Other Identifier
- 991019170462204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Computer Science, Theory & Methods