Inproceedings,
Identifying relevant event content for real-time event detection
Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, page 395-398. (August 2014)
DOI: 10.1109/ASONAM.2014.6921616
Abstract
A variety of event detection algorithms for microblog services have been proposed, but their accuracy relies on the microblog feeds they analyse. Existing research explores datasets that are collected using either a set of manually predefined terms or information from external sources. These methods fail to provide comprehensive and quality feeds for real-time event detection. In this paper, we present a novel adaptive keyword identification approach to retrieve a greater amount of event relevant content. This approach continuously monitors emerging hashtags and rates them by their similarity to specific pre-defined event hashtags using TF-IDF vectors. Top rated emerging hashtags are added as filter criteria in real time. By comparing our proposed approach, called CETRe (Content-based Event Tweet Retrieval) with an existing baseline approach applied to real-world events, we show that CETRe not only identifies event topics and contents, but also enables better event detection.
Tags
- adaptivecrawler
- content
- dblp
- detetction
- event
- focusedcralwer
- for
- identifying
- real-event
- relevant
- streaming
