Name
Abstract | ||
|---|---|---|
Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116 | 19TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2021) |
Keywords | DocType | ISSN |
event detection, cluster analysis, temporal information, social media | Conference | 2158-9178 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaofu Zhang | 1 | 0 | 0.68 |
| Lianzhong Liu | 2 | 0 | 0.68 |
| Zihang Huang | 3 | 0 | 0.68 |
| Lihua Han | 4 | 0 | 0.68 |
| Shuhai Wang | 5 | 0 | 0.68 |
| Tongge Xu | 6 | 2 | 1.37 |
| Jingyi Zhang | 7 | 0 | 0.34 |
| Yangyang Li | 8 | 4 | 2.74 |
| Yifeng Liu | 9 | 0 | 3.72 |
| Md Zakirul Alam Bhuiyan | 10 | 0 | 0.68 |