Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques
This work addresses the need for automated disaster response tools by providing incremental improvements in processing social media data for flood monitoring.
The paper tackled the problem of classifying flood-related Twitter posts and extracting location information from them for disaster management, achieving F1-scores up to 0.7970 for relevance classification and 0.6744 for location extraction.
This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.