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Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison
Nilani Algiriyage
author
Rangana Sampath
author
Raj Prasanna
author
Kristin Stock
author
Emma Hudson-Doyle
author
David Johnston
author
2021
Virginia Tech
Blacksburg, VA (USA)
English
Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research.
Tweet Classification
Machine Learning
Deep Learning
Disasters
rangika.nilani@gmail.com
exported from refbase (http://idl.iscram.org/show.php?record=2368), last updated on Tue, 13 Jul 2021 18:38:54 +0200
text
http://idl.iscram.org/files/nilanialgiriyage/2021/2368_NilaniAlgiriyage_etal2021.pdf
NilaniAlgiriyage_etal2021
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
Iscram 2021
Anouck Adrot
editor
Rob Grace
editor
Kathleen Moore
editor
Christopher W. Zobel
editor
18th International Conference on Information Systems for Crisis Response and Management
2021
Virginia Tech
Blacksburg, VA (USA)
conference publication
731
743
978-1-949373-61-5
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