Event Detection and Identification of Influential Spreaders in Social Media Data Streams
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 h...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020004 |
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| _version_ | 1832573604357734400 |
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| author | Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang |
| author_facet | Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang |
| author_sort | Leilei Shi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1e5b2a7d3fe64b1194e6a4272b9fc462 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-1e5b2a7d3fe64b1194e6a4272b9fc4622025-02-02T03:44:40ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-03-0111344610.26599/BDMA.2018.9020004Event Detection and Identification of Influential Spreaders in Social Media Data StreamsLeilei Shi0Yan Wu1Lu Liu2Xiang Sun3Liang Jiang4<institution content-type="dept">School of Computer Science and Telecommunication Engineering</institution>, <institution>Jiangsu University</institution>, <city>Zhenjiang</city> <postal-code>212013</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Telecommunication Engineering</institution>, <institution>Jiangsu University</institution>, <city>Zhenjiang</city> <postal-code>212013</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Telecommunication Engineering</institution>, <institution>Jiangsu University</institution>, <city>Zhenjiang</city> <postal-code>212013</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Telecommunication Engineering</institution>, <institution>Jiangsu University</institution>, <city>Zhenjiang</city> <postal-code>212013</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Telecommunication Engineering</institution>, <institution>Jiangsu University</institution>, <city>Zhenjiang</city> <postal-code>212013</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020004event detectionmicroblogginghypertext-induced topic search (hits)latent dirichlet allocation (lda)identification of influential spreader |
| spellingShingle | Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang Event Detection and Identification of Influential Spreaders in Social Media Data Streams Big Data Mining and Analytics event detection microblogging hypertext-induced topic search (hits) latent dirichlet allocation (lda) identification of influential spreader |
| title | Event Detection and Identification of Influential Spreaders in Social Media Data Streams |
| title_full | Event Detection and Identification of Influential Spreaders in Social Media Data Streams |
| title_fullStr | Event Detection and Identification of Influential Spreaders in Social Media Data Streams |
| title_full_unstemmed | Event Detection and Identification of Influential Spreaders in Social Media Data Streams |
| title_short | Event Detection and Identification of Influential Spreaders in Social Media Data Streams |
| title_sort | event detection and identification of influential spreaders in social media data streams |
| topic | event detection microblogging hypertext-induced topic search (hits) latent dirichlet allocation (lda) identification of influential spreader |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020004 |
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