Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Author:

Malik Muhammad Shahid Iqbal1,Younas Muhammad Zeeshan2,Jamjoom Mona Mamdouh3,Ignatov Dmitry I.1

Affiliation:

1. Department of Computer Science, National Research University Higher School of Economics, Moscow, Russia

2. Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan

3. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Abstract

Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

Funder

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

PeerJ

Reference41 articles.

1. MEDIC: a multi-task learning dataset for disaster image classification;Alam;Neural Computing and Applications,2023

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3. Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria;Alam;Behaviour & Information Technology,2020

4. CrisisBench: benchmarking crisis-related social media datasets for humanitarian information processing;Alam,2021

5. Rumour identification on Twitter as a function of novel textual and language-context features;Ali;Multimedia Tools and Applications,2023

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