Name
Abstract | ||
|---|---|---|
Exploration and discovery of the relationship between social media activities and rare-event evolution have been investigated by many researchers in recent years. Their investigations have revealed the existence of such relationship. Furthermore, some researchers regard finding either a temporal or spatial pattern of social media activities as a way to evaluate the evolution of rare event. However, most of them fail to deduce an accurate time point when a rare event highly impacts social media activities. This paper concentrates on the intensity of information volume and proposes an innovative data processing method based on clustering algorithms. The proposed method can characterize the evolution of a rare event in the real world by analyzing social media activities in the virtual world. This exploration contributes to study changes of social media activities in the time domain. A case study is based on Hurricane Sandy that occurred in 2012. Social media data collected from Twitter during its arrival time span are adopted to evaluate the feasibility and effectiveness of our proposed method. First, this paper confirms that a strong correlation between a rare event and social media activities does exist. Next, it uncovers that a time difference does exist between the real and virtual worlds. In general, this paper gives a novel idea that deduces a temporal pattern of social media activities during the occurrence of rare events. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSS.2019.2898774 | IEEE Transactions on Computational Social Systems |
Keywords | Field | DocType |
Social networking (online),Clustering algorithms,Data processing,Hurricanes,Time-domain analysis,Measurement,Urban areas | Time domain,Data mining,Metaverse,Data processing,Social media,Time point,Computer science,Event evolution,Cluster analysis,Rare events | Journal |
Volume | Issue | ISSN |
6 | 2 | 2329-924X |
Citations | PageRank | References |
3 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyu Lu | 1 | 10 | 5.31 |
| MengChu Zhou | 2 | 8989 | 534.94 |
| Liang Qi | 3 | 156 | 27.14 |
| Haoyue Liu | 4 | 21 | 2.38 |