The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Practical extraction of disaster-relevant information from social media
Abstract
During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
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Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
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Zhang, C., Fan, C., Yao, W., Hu, X., & Mostafavi, A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.004 · Cited by 293
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- Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., & Meier, P. (2013). Practical extraction of disaster-relevant information from social media. Proceedings of the 22nd International Conference on World Wide Web. https://doi.org/10.1145/2487788.2488109
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