Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

1-2023

Abstract

Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language model is insufficient to identify situation awareness information. Fine-tuning by using datasets of various event types and vocabulary extension is essential to adapt a DistilBERT model for real-world situational awareness detection.

Keywords

pretrained models, situational awareness, BERT, fine tuning, vocabulary extension

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

2023 56th Hawaii International Conference on System Sciences: Hawaii, January 3-6: Proceedings

First Page

2110

Last Page

2119

Publisher

IEEE Computer Society

City or Country

Hawaii

Copyright Owner and License

Authors

Additional URL

https://hdl.handle.net/10125/102894

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