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.
%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
}