1.1
1
xml
info:srw/schema/1/dc-v1.1
Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison
Nilani Algiriyage
Rangana Sampath
Raj Prasanna
Kristin Stock
Emma Hudson-Doyle
David Johnston
Anouck Adrot
Rob Grace
Kathleen Moore
Christopher W. Zobel
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.
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Identifying%20Disaster-related%20Tweets%3A%20A%20Large-Scale%20Detection%20Model%20Comparison&stitle=Iscram%202021&issn=978-1-949373-61-5&date=2021&spage=731&epage=743&aulast=Nilani%20Algiriyage&au=Rangana%20Sampath&au=Raj%20Prasanna&au=Kristin%20Stock&au=Emma%20Hudson-Doyle&au=David%20Johnston&pub=Virginia%20Tech&place=Blacksburg%2C%20VA%20%28USA%29&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2368
citekey:NilaniAlgiriyage_etal2021
citation:Nilani Algiriyage, Rangana Sampath, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, & David Johnston. (2021). Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 731-743). Blacksburg, VA (USA): Virginia Tech.
2021
ConferencePaper
text
Tweet Classification, Machine Learning, Deep Learning, Disasters
file:http://idl.iscram.org/files/nilanialgiriyage/2021/2368_NilaniAlgiriyage_etal2021.pdf
Virginia Tech
English
978-1-949373-61-5
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
2021
731
743
1