Name
Abstract | ||
|---|---|---|
Burstiness has been one of the most important criteria for extracting topics and events from documents posted on social media. Recently, researchers are focusing on extracting geolocal topics and events from such social documents because of the increasing number of geo-annotated documents (e.g., geo-tagged tweets on Twitter). In our previous work, we developed a method for identifying local temporal burstiness to detect local hot keywords considering the users' location. The previous method is based on Kleinberg's temporal burst detection algorithm, which presupposes that the rate of posting remains constant. However, this leads to a difference in bursty periods depending on public awareness. To address this issue, in this paper, we propose a novel method for identifying local temporal burstiness by using the MACD-histogram-based temporal burst detection algorithm. The MACD-histogram-based temporal burst detection algorithm is based on the trend analysis of stock prices. To compare the proposed method with the previous method, we conducted experiments using actual burst detection in geo-tagged documents. The experiments revealed that the proposed method can identify local temporal burstiness on the basis of public awareness. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SMC.2015.466 | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
Keywords | Field | DocType |
Burst detection, Geo-tagged tweet, MACD histogram, Social media | Data mining,Histogram,Time series,MACD,Public awareness,Social media,Computer science,Burstiness,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keiichi Tamura | 1 | 37 | 13.86 |
| Matsui, T. | 2 | 0 | 0.68 |
| H. Kitakami | 3 | 94 | 49.68 |
| Tatsuhiro Sakai | 4 | 10 | 4.71 |