Online Bursty Events Detection Based on Emoticons

Zhang, Lu-Min; Jia, Yan; Zhou, Bin; Zhao, Jinhui; Hong, Feng

doi:10.3724/sp.j.1016.2013.01659

Cited by 4 publications

(2 citation statements)

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“…(3) The third type of bursty event detection method models the trend of emotional or user social behavior, and uses its difference between adjacent time windows to detect events. such as the Xu et al [8] conducts sentiment classification quantitative analysis on perceived topics for bursty event detection; Zhang, et al [9] calculates the relative entropy of emotion in two adjacent time windows for event detection. Mu et al [10] proposes a lifecycle-based event detection framework based on the trend of public sentiment.…”

Section: Related Workmentioning

confidence: 99%

Sina Weibo Bursty Event Detection Method

Chen

Liang

2019

IEEE Access

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In response to the recent frequent outbreaks of bursty events, if the supervision is not strengthened, the grievances caused by it will have an extremely negative impact on society. Therefore, how to effectively detect bursty events in social networks has become the focus of research. In order to eliminate the interference of local daily events generated by noisy data, this paper proposes a bursty event detection method for quantifying the influence of microblog text. Through the analysis of high-impact microblogs, the burst words are mined, the potential bursty event data sets are constructed, and the k-means cluster analysis method is applied to detect the event. In order to verify the method, this paper compares with the commonly used detection methods on the real dataset. The experimental results show that the method has certain improvement in accuracy and efficiency.

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Section: Related Workmentioning

confidence: 99%

Sina Weibo Bursty Event Detection Method

Chen

Liang

2019

IEEE Access

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“…Y. Liang et al [7] detected the microblog hot events based on the distribution of emotion. L. M. Zhang et al [16] adopted emoticons to detect online bursty events. Q. M. Diaoet al [2] combined temporal information and user relationships when detecting the microblog bursty topics, which resulted in a better performance.…”

Section: Related Workmentioning

confidence: 99%

What Is the Whole Development Process? Subevent Detection using Micro Index and Local Clustering

Zhao¹

2018

IJPE

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Users can easily obtain a massive amount of news stories related to an event. But, often the obtained stories are fragmented and can only reflect certain aspects of the event. Detecting the subevents automatically is important for users to understand the whole development process of the event. Motivated by the co-evolution between the event and the opinions about it, we firstly propose to adopt Micro Index and give a dynamic time window construction method based on the recognition of the peaks of the Micro Index Curve. Secondly, we propose a two-stage subevent detection method based on local clustering and classification. And finally, we introduce the news stories about "Luo Yixiao Event" and "Shandong Illegal Vaccines Event", which are two recent hot events, and use them to evaluate the proposed methods. It is found that the proposed methods are successful.

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