Steds: Social Media Based Transportation Event Detection with Text Summarization

Kaiqun Fu, Chang-Tien Lu

Abstract

Ubiquitous user-input contents on social media and online services have generated a tremendous amount of information. Such information has great potential applications in various areas such as events detection and text summarization. In this paper, a social media based traffic status monitoring system is established. The system is initiated by a transportation related keyword generation process. Then an association rules based iterative query expansion algorithm is applied to extract real time transportation related tweets for incident management purpose. We also confirm the feasibility of summarizing the redundant tweets to generate concise and comprehensible textual contents. Comparison results show that our query expansion method for tweets extraction outperforms the previous ones. Analysis and case studies further demonstrate the practical usefulness of our tweets summarization algorithm.