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Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams
- Source :
- Expert Systems with Applications. 39:13338-13356
- Publication Year :
- 2012
- Publisher :
- Elsevier BV, 2012.
-
Abstract
- Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize the social-media messages to characterize real-world events through mining their contents and extracting essential features for relatedness analysis. On one hand, we established an online clustering approach on Twitter microblogs for detecting emerging events, and meanwhile we performed event relatedness evaluation using an unsupervised clustering approach. On the other hand, we developed a supervised learning model to create extensible measure metrics for offline evaluation of event relatedness. By means of supervised learning, our developed measure metrics are able to compute relatedness of various historical events, allowing the event impacts on specified domains to be quantitatively measured for event comparison. By combining the strengths of both methods, the experimental results showed that the combined framework in our system is sensible for discovering more unknown knowledge about event impacts and enhancing event awareness.
- Subjects :
- Event (computing)
Microblogging
business.industry
Supervised learning
General Engineering
Social mining
Machine learning
computer.software_genre
Computer Science Applications
Artificial Intelligence
Content (measure theory)
Social media
Artificial intelligence
business
Unsupervised clustering
Cluster analysis
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........f9dd022d44f303fef7f5b454724734c7