  In the field of crisis/disaster informatics, social media is increasingly
being used for improving situational awareness to inform response and relief
efforts. Efficient and accurate text classification tools have been a focal
area of investigation in crisis informatics. However, current methods mostly
rely on single-label text classification models, which fails to capture
different insights embedded in dynamic and multifaceted disaster-related social
media data. This study introduces a novel approach to disaster text
classification by enhancing a pre-trained Large Language Model (LLM) through
instruction fine-tuning targeted for multi-label classification of
disaster-related tweets. Our methodology involves creating a comprehensive
instruction dataset from disaster-related tweets, which is then used to
fine-tune an open-source LLM, thereby embedding it with disaster-specific
knowledge. This fine-tuned model can classify multiple aspects of
disaster-related information simultaneously, such as the type of event,
informativeness, and involvement of human aid, significantly improving the
utility of social media data for situational awareness in disasters. The
results demonstrate that this approach enhances the categorization of critical
information from social media posts, thereby facilitating a more effective
deployment for situational awareness during emergencies. This research paves
the way for more advanced, adaptable, and robust disaster management tools,
leveraging the capabilities of LLMs to improve real-time situational awareness
and response strategies in disaster scenarios.
