Reconstruction of Unfolding Sub-Events From Social Media Posts
Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utili...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.918663/full |
| _version_ | 1818248146252201984 |
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| author | Ren-De Li Qiang Guo Xue-Kui Zhang Jian-Guo Liu |
| author_facet | Ren-De Li Qiang Guo Xue-Kui Zhang Jian-Guo Liu |
| author_sort | Ren-De Li |
| collection | DOAJ |
| description | Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media. |
| first_indexed | 2024-12-12T15:15:57Z |
| format | Article |
| id | doaj.art-a37a0aa3d0b74163a9bd9ca411cb700f |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-12-12T15:15:57Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-a37a0aa3d0b74163a9bd9ca411cb700f2022-12-22T00:20:30ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-06-011010.3389/fphy.2022.918663918663Reconstruction of Unfolding Sub-Events From Social Media PostsRen-De Li0Qiang Guo1Xue-Kui Zhang2Jian-Guo Liu3Library and Business School, University of Shanghai for Science and Technology, Shanghai, ChinaLibrary and Business School, University of Shanghai for Science and Technology, Shanghai, ChinaInstitute of Journalism, Shanghai Academy of Social Science, Shanghai, ChinaInstitute of Accounting and Finance, Shanghai University of Finance and Economics, Shanghai, ChinaEvent detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.https://www.frontiersin.org/articles/10.3389/fphy.2022.918663/fullsub-event miningsub-event detectionsub-event correlationsub-event summarysub-event evolutionexpert knowledge |
| spellingShingle | Ren-De Li Qiang Guo Xue-Kui Zhang Jian-Guo Liu Reconstruction of Unfolding Sub-Events From Social Media Posts Frontiers in Physics sub-event mining sub-event detection sub-event correlation sub-event summary sub-event evolution expert knowledge |
| title | Reconstruction of Unfolding Sub-Events From Social Media Posts |
| title_full | Reconstruction of Unfolding Sub-Events From Social Media Posts |
| title_fullStr | Reconstruction of Unfolding Sub-Events From Social Media Posts |
| title_full_unstemmed | Reconstruction of Unfolding Sub-Events From Social Media Posts |
| title_short | Reconstruction of Unfolding Sub-Events From Social Media Posts |
| title_sort | reconstruction of unfolding sub events from social media posts |
| topic | sub-event mining sub-event detection sub-event correlation sub-event summary sub-event evolution expert knowledge |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.918663/full |
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