Relevance Classification of Flood-related Twitter Posts via Multiple Transformers

Wisal Mukhtiar, Waliiya Rizwan, Aneela Habib, Yasir Saleem Afridi, Laiq Hasan +1 more
1/1/2023
cs.CL
transformer-attention

No confident code implementation found for this paper.

Abstract

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics

84% match1 repo82024

Comparative Analysis of Transformer Models in Disaster Tweet Classification for Public Safety

83% match2025

Multi-Stakeholder Disaster Insights from Social Media Using Large Language Models

83% match2025

Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning

82% match2024

QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment

81% match2024

RweetMiner: Automatic identification and categorization of help requests on twitter during disasters

80% match2023

Cite this paper

@article{mukhtiar2023relevance,
  title  = {Relevance Classification of Flood-related Twitter Posts via Multiple Transformers},
  author = {Wisal Mukhtiar and Waliiya Rizwan and Aneela Habib and Yasir Saleem Afridi and Laiq Hasan and Kashif Ahmad},
  year   = {2023},
  eprint = {2301.00320},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.00320v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2301.00320.svg)](https://codeofpaper.com/paper/2301.00320)

Discussion