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Evidential fine-grained event localization using Twitter
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DOI：10.1016/j.ipm.2019.05.006.png)
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Abstract
En 中文
The widespread popularity and worldwide application of social networks have raised interest in the analysis of content created on the networks. One such analytical application and aspect of social networks, including Twitter, is identifying the location of various political and social events, natural disasters and so on. The present study focuses on the localization of traffic accidents. Outdated and inaccurate information in user profiles, the absence of location data in tweet texts, and the limited number of geotagged posts are among the challenges tackled by location estimation. Adopting the Dempster-Shafer Evidence Theory, the present study estimates the location of accidents using a combination of user profiles, tweet texts, and the place attachments in tweets. The results indicate improved performance regarding error distance and average error distance compared to previously developed methods. The proposed method in this study resulted in a reduced error distance of 26%.
Keywords:
Social media analysis
Event localization
Event detection
Dempster Shafer theory
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