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AuthorPradeep Kumar, Roy
AuthorKumar, Abhinav
AuthorSingh, Jyoti Prakash
AuthorDwivedi, Yogesh Kumar
AuthorRana, Nripendra Pratap
AuthorRaman, Ramakrishnan
Available date2023-06-08T08:16:22Z
Publication Date2021-09-22
Publication NameSustainable Cities and Society
Identifierhttp://dx.doi.org/10.1016/j.scs.2021.103363
CitationRoy, P. K., Kumar, A., Singh, J. P., Dwivedi, Y. K., Rana, N. P., & Raman, R. (2021). Disaster related social media content processing for sustainable cities. Sustainable Cities and Society, 75, 103363.
ISSN2210-6707
URIhttps://www.sciencedirect.com/science/article/pii/S2210670721006387
URIhttp://hdl.handle.net/10576/44134
AbstractThe current study offers a hybrid convolutional neural networks (CNN) model that filters relevant posts and categorises them into several humanitarian classifications using both character and word embedding of textual content. The distinct embeddings for words and characters are used as input to the CNN model’s various channels. A hurricane, flood, and wildfire dataset are used to validate the proposed model. The model performed similarly across all datasets, with the F1-score ranging from 0.66 to 0.71. Because it uses existing social media posts and may be used as a layer with any social media, the model provides a sustainable solution for disaster analysis. With domain-specific training, the suggested approach can be used to locate useful information in other domains such as traffic accidents and civil unrest also.
Languageen
PublisherElsevier
SubjectDisaster
Twitter
Deep learning
CNN
Word embedding
Character embedding
TitleDisaster related social media content processing for sustainable cities
TypeArticle
Volume Number75
ESSN2210-6715
dc.accessType Abstract Only


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