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- W4384916193 abstract "Twitter serves as a resource for information and a platform for knowledge sharing during emergencies and natural disasters. It is primarily used to update and share information about the progress of disaster recovery efforts started by disaster management and humanitarian organizations. People can communicate facts they have observed in real time via smartphones. Twitter information is not always accurate and can sometimes result in incorrect conclusions. In this work, the disaster management information is analyzed using the bidirectional encoder representations from transformers (BERT) model. It aids mitigation teams in the organization and planning of efficient catastrophe responses, as well as the eventual implementation of automated technologies for managing crisis situations. The major goal of this paper is to categorize tweets made during a disaster as reliable or unreliable by utilizing text processing and natural language processing methods. A dataset of 10,000 tweets was used in this study to train the model. The most effective model is chosen from a range of NLP-based classification techniques based on performance factors." @default.
- W4384916193 created "2023-07-21" @default.
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- W4384916193 date "2023-01-01" @default.
- W4384916193 modified "2023-09-27" @default.
- W4384916193 title "Dtweet: Disaster Tweet Analysis Using Deep Learning Techniques" @default.
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- W4384916193 doi "https://doi.org/10.1007/978-981-99-3608-3_23" @default.
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