Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques

TL;DR

This paper develops NLP-based methods to classify flood-related tweets and extract location information, achieving high accuracy in disaster-related social media analysis for the MediaEval 2022 task.

Contribution

It introduces multiple BERT-based models for relevance classification and location extraction, demonstrating effective solutions for disaster tweet analysis.

Findings

BERT-based models achieved up to 0.7970 F1-score in relevance classification.

RoBERTa outperformed other models in location extraction with 0.6744 F1-score.

The proposed methods effectively differentiate flood-related posts and extract locations from Twitter texts.

Abstract

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.