Event identification for social streams using keyword-based evolving graph sequences

2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) Pub Date : 2013-08-25 DOI:10.1145/2492517.2492528

Elizabeth Kwan, Pei-Ling Hsu, Jheng-He Liang, Yi-Shin Chen

引用次数: 15

Abstract

Social networks, which have become extremely popular nowadays, contain a tremendous amount of user-generated content about real-world events. This user-generated content can naturally reflect the real-world event as they happen, and sometimes even ahead of the newswire. The goal of this work is to identify events from social streams. A model called “keyword-based evolving graph sequences” (kEGS) is proposed to capture the characteristics of information propagation in social streams. The experimental results show the usefulness of our approach in identifying real-world events in social streams.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于关键字演化图序列的社交流事件识别

如今已经非常流行的社交网络包含了大量用户生成的关于现实世界事件的内容。这种用户生成的内容可以自然地反映现实世界中发生的事件，有时甚至比新闻专线还要早。这项工作的目标是从社交流中识别事件。提出了一种基于关键词的进化图序列(kEGS)模型来捕捉社交流中信息传播的特征。实验结果表明，我们的方法在识别社交流中的现实世界事件方面是有用的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)

自引率

0.00%

发文量

期刊最新文献

Statistical analysis and implications of SNS search in under-developed countries Identifying unreliable sources of skill and competency information Assessing group cohesion in homophily networks Exploiting online social data in ontology learning for event tracking and emergency response Event identification for social streams using keyword-based evolving graph sequences