Disaster Rescue Communication Using Mobile Devices, Social Media, and Artificial Intelligence
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-8388-7_175
Reference58 articles.
1. Alam, F., Ofli, F., & Imran, M. (2018). CrisisMMD: Multimodal twitter datasets from natural disasters. In Proceedings of the twelfth International AAAI Conference on Web and Social Media (ICWSM 2018). https://arxiv.org/abs/1805.00713
2. Anderson, J., Saez, G. C., Anderson, K. M., Palen, L., & Morss, R. (2019). Incorporating context and location into social media analysis: A scalable, cloud-based approach for more powerful data science. In Proceedings of the 52nd Hawaii international conference on system sciences. https://aisel.aisnet.org/hicss-52/dsm/data_mining/8/
3. Bean, H., Cruz, A. M., Shimizu, M., Stephens, K. K., McGlone, M., & Strover, S. (2021a). Mobile alert and warning in the United States and Japan: Confronting the challenges of international harmonization. International Journal of Disaster Risk Science, 12(6), 928–934. https://doi.org/10.1007/s13753-021-00380-4
4. Bean, H., Grevstad, N., Meyer, A., & Koutsoukos, A. (2021b). Exploring whether wireless emergency alerts can help impede the spread of Covid-19. Journal of Contingencies and Crisis Management, 1–19. https://doi.org/10.1111/1468-5973.12376
5. Brennan, S., Sadilek, A., & Kautz, H. (2013). Towards understanding global spread of disease from everyday interpersonal interactions. In Proceedings of IJCAI. AAAI Press, 2783–2789. https://doi.org/10.5555/2540128.2540530.
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