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Open Access

Event Detection and Identification of Influential Spreaders in Social Media Data Streams

Leilei Shi, Yan Wu, Lu Liu( ), Xiang Sun, Liang Jiang
School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China.
Department of Computing and Mathematics, University of Derby, UK.
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Abstract

Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA). TD-HITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information. The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.

Keywords

event detectionmicrobloggingHypertext-Induced Topic Search (HITS)Latent Dirichlet Allocation (LDA)identification of influential spreader

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Big Data Mining and Analytics
Volume 1 Issue 1,
March 2018
Pages 34-46
DOI: 10.26599/BDMA.2018.9020004
Cite this article:
Shi L, Wu Y, Liu L, et al. Event Detection and Identification of Influential Spreaders in Social Media Data Streams. Big Data Mining and Analytics, 2018, 1(1): 34-46. https://doi.org/10.26599/BDMA.2018.9020004

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Received: 09 September 2017
Accepted: 29 November 2017
Published: 25 January 2018
© The author(s) 2018
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