Abstract
This paper presents a system for classifying disaster-related tweets. The focus is on Twitter data generated before, during, and after Hurricane Sandy, which impacted New York in the fall of 2012. We propose an annotation schema for identifying relevant tweets as well as the more fine-grained categories they represent, and develop feature-rich classifiers for relevance and fine-grained categorization.
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CITATION STYLE
Stowe, K., Paul, M., Palmer, M., Palen, L., & Anderson, K. (2016). Identifying and Categorizing Disaster-Related Tweets. In EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the 4th International Workshop on Natural Language Processing for Social Media, SocialNLP 2016 (pp. 1–6). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-6201
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