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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ming-Hung Wang Chin-Laung Lei |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Ming-Hung Wang; Chin-Laung Lei) |
| Abstract | Sunflower Movement is one of the most influential social movements in Taiwan over the past few decades. In order to protest the review process at the legislature, the protesters entered and occupied the building of the Legislative Yuan (the parliament) of Taiwan on March 18, 2014, without any sign in advance. This action shook the government and caught a high level of attention in Taiwan. People discussed the action from different viewpoints and showed their supporting and opposition on the movement in daily life as well as on social media sites. However, the information is in chaos since a large number of articles had been published during the movement. In order to realize the social discussions in a comprehensive way, major issues such as extracting notable threads and finding important authors among social sites need to be addressed. In this paper, we provide methodologies to quantify and predict the polarity of each article, we also present a consensus-based approach to identify influential authors on the social forum. From our results, our proposal is effective and efficient for identifying information and authors that are worthy of attention through the social movement. |
| Starting Page | 1664 |
| Ending Page | 1669 |
| File Size | 315716 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479986972 |
| DOI | 10.1109/SMC.2015.293 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proposals Social network services Government Media Data mining Peer-to-peer computing Predictive models influential author identification information extraction article polarity prediction |
| Content Type | Text |
| Resource Type | Article |
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