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- W2900388514 abstract "Nowadays, Twitter has become one of the fastest-growing microblogging services, analyzing this rich and continuously user-generated content can reveal unprecedentedly valuable knowledge. In this paper, we propose a novel two-stage system to detect and track events from tweets by integrating an LDA-based approach and an efficient density-contour based spatio-temporal clustering approach. In the proposed system, we first divide the geo-tagged tweet stream into temporal time windows; next, events are identified as topics in tweets using an LDA-based topic discovery step; then, each tweet is assigned an event label; next, a density-contour based spatio-temporal clustering approach is employed to identify spatio-temporal event clusters. Moreover, topic continuity is established by calculating KL-divergences between topics and spatio-temporal continuity is established by a family of newly formulated spatial cluster distance functions. Moreover, the density-contour based approach considers two types of densities: absolute density and relative density to identify event clusters where either there is a high density of event tweets or there is a high percentage of event tweets. We evaluate our approach using real-world data collected from Twitter, and the experimental results show that the proposed system can not only detect and track events effectively but also discover interesting patterns from geotagged tweets." @default.
- W2900388514 created "2018-11-16" @default.
- W2900388514 creator A5005359731 @default.
- W2900388514 creator A5056584154 @default.
- W2900388514 date "2018-09-01" @default.
- W2900388514 modified "2023-09-26" @default.
- W2900388514 title "A Novel Two-Stage System for Detecting and Tracking Events in Twitter" @default.
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- W2900388514 doi "https://doi.org/10.1109/aike.2018.00019" @default.
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