%0 Conference Paper %1 6921616 %A Wang, X. %A Tokarchuk, L. %A Poslad, S. %B Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on %D 2014 %K adaptiveCrawler focusedCralwer streaming twitter %P 395-398 %R 10.1109/ASONAM.2014.6921616 %T Identifying relevant event content for real-time event detection %U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6921616&reason=concurrency %X 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.

@inproceedings{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.}, added-at = {2016-05-20T13:31:06.000+0200}, author = {Wang, X. and Tokarchuk, L. and Poslad, S.}, biburl = {https://www.bibsonomy.org/bibtex/2098cf28bf1211ee54852c2cc9c61884b/asmelash}, booktitle = {Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on}, description = {IEEE Xplore Abstract - Identifying relevant event content for real-time event detection}, doi = {10.1109/ASONAM.2014.6921616}, interhash = {093b780cb8bd31c9c5f8bd01076af759}, intrahash = {098cf28bf1211ee54852c2cc9c61884b}, keywords = {adaptiveCrawler focusedCralwer streaming twitter}, month = aug, pages = {395-398}, timestamp = {2016-05-20T13:31:06.000+0200}, title = {Identifying relevant event content for real-time event detection}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6921616&reason=concurrency}, year = 2014 }