Mining phase evolution for hot topics: A case study from multiple social media platforms
Ruoran Liu1,2; Qiudan Li1; Can Wang1,2; Lei Wang1; Daniel Dajun Zeng1,2,3; Hongyuan Ma4
2017
Conference Name2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017)
Pages2814-2819
Conference DateOctober 5-8, 2017
Conference PlaceCanada
AbstractMonitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this problem is how to identify temporal pattern and mine topic of different phases automatically. In this paper, we propose a unified phase evolution mining model, it firstly identifies the temporal patterns of phases based on k-means and empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media platform to understand the event evolution in a more comprehensive way. Experimental results show the characteristics of event evolution on different social media platforms and verify the efficacy of the proposed model.
KeywordPhase Evolution K-means Burst Detection Textrank Social Media
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19880
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorQiudan Li
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing, China
3.University of Arizona Tucson, Arizona, USA
4.CNCERT/CC, Beijing, China
Recommended Citation
GB/T 7714
Ruoran Liu,Qiudan Li,Can Wang,et al. Mining phase evolution for hot topics: A case study from multiple social media platforms[C],2017:2814-2819.
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