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Reference130 articles.
1. Abavisani M, Wu L, Hu S, Tetreault J, Jaimes A. Multimodal categorization of crisis events in social media. arXiv (2020).
2. Acerbo FS, Rossi C. Filtering informative tweets during emergencies: a machine learning approach. In: I-TENDER 2017—Proceedings of the 2017 1st CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief pp. 1–6 (2017). https://doi.org/10.1145/3152896.3152897.
3. Agarwal M, Leekha M, Sawhney R, Shah RR. Crisis-DIAS: towards multimodal damage analysis—deployment, challenges and assessment. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020;34(01):346–53. https://doi.org/10.1609/aaai.v34i01.5369.
4. Ahmad K, Pogorelov K, Riegler M, Ostroukhova O, Halvorsen P, Conci N, Dahyot R. Automatic detection of passable roads after floods in remote sensed and social media data. Signal Process Image Commun. 2019;74((December 2018)):110–8. https://doi.org/10.1016/j.image.2019.02.002.
5. Aipe A, Ekbal A, S, MN, Kurohashi S. Linguistic feature assisted deep learning approach towards multi-label classification of crisis related tweets. In: Boersma K, Tomaszewski BM (eds.) Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management, Rochester, NY, USA, May 20-23, 2018. ISCRAM Association (2018). http://idl.iscram.org/files/alanaipe/2018/1592_AlanAipe_etal2018.pdf.
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