Name
Abstract | ||
|---|---|---|
Once a disaster occurs, people discuss various topics in social media such as electronic bulletin boards, SNSs and video services, and their decision-making tends to be affected by discussions in social media. Under the circumstance, a mechanism to detect topics in social media has become important. This paper targets the East Japan Great Earthquake, and proposes a time series topic detection method based on modularity measure which shows the quality of a division of a network into modules or communities. Our proposed method clarifies emerging topics from social media messages by computing the modularity and analyzing them over time, and visualizes topic structures. An experimental result by actual social media data about the East Japan Great Earthquake is also shown. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/978-1-61499-177-9-110 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Topic detection,The East Japan Great Earthquake,Modularity,Data mining | Data science,Discrete mathematics,Mathematics,Modularity | Conference |
Volume | ISSN | Citations |
251 | 0922-6389 | 1 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takako Hashimoto | 1 | 50 | 18.47 |
| Tetsuji Kuboyama | 2 | 140 | 29.36 |
| Yukari Shirota | 3 | 1 | 0.39 |