Spatio-temporal characteristics of bursty words in Twitter streams
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
| Hi-index | 0.00 |
Twitter has become popular among researchers as a means to detect various kinds of events. Several attempts were made to detect trends, real world events, news, earthquakes and others with satisfying results. However they do not perform well on finding local events such as release parties, musicians in a park, or art exhibitions. Many of the local events that were found by algorithms of existing work were not related to an event but to locations, global events, or just common words. In this paper, we introduce Event Radar, a novel local event detection method to improve the precision by analyzing seven day historic Tweet data. We estimate the average Tweet frequency of keywords per day in and around a potential event area and use these estimations to classify whether the keywords are related to a local event. The proposed scheme achieves a precision rate of 68% which is a significant improvement compared to related work that states a precision rate of 25.5%.