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Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma
Research article (International Journal of Digital Earth, 2019) · cited 112× · AI/ML
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma
Summary
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma is a scholarly article[1].
Key Facts
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma. Retrieved May 24, 2026, from https://4ort.xyz/entity/identifying-disaster-related-tweets-and-their-semantic-spatial-and-temporal-context-using-deep-learning-natural-language
MLA“Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/identifying-disaster-related-tweets-and-their-semantic-spatial-and-temporal-context-using-deep-learning-natural-language.
BibTeX@misc{4ortxyz_identifying-disaster-related-tweets-and-their-semantic-spatial-and-temporal-context-using-deep-learning-natural-language_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma}}, year = {2026}, url = {https://4ort.xyz/entity/identifying-disaster-related-tweets-and-their-semantic-spatial-and-temporal-context-using-deep-learning-natural-language}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma — https://4ort.xyz/entity/identifying-disaster-related-tweets-and-their-semantic-spatial-and-temporal-context-using-deep-learning-natural-language (retrieved 2026-05-24)
