CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods

Authors : Halit Çetiner, Hakan Yüksel

Pages : 563-576

Doi:10.17714/gumusfenbil.1653072

View : 55 | Download : 32

Publication Date : 2025-06-15

Article Type : Research Paper

Abstract :Twitter, one of the social media platforms, is one of the reliable sources that allows everyone to express their thoughts and ideas online. In this article, we focus on analysing and analysing the text content of tweets on the Twitter platform in extraordinary situations such as possible disasters or disasters. As a result of real-time information from the Twitter platform, it is possible to help people in possible disaster situations and automatically direct emergency teams. In order to prepare the ground for the realization of these possible scenarios, it is necessary to perform high performance classification by identifying disaster-related content from thousands of raw text content. In this paper, we propose a CBLTwitter model that classifies disasters by increasing the weight scores of their significant values that can capture local patterns and contextual dependencies in raw tweet information. The proposed CBLTwitter model investigates the effectiveness of a contextual word embedder called Bidirectional Encoder Representations from Transformers (BERT) in predicting disasters from Twitter data. In addition, BERT results are compared with the results obtained from independent word embedding methods called Word2Vec and Global Vectors for Word Representation (GloVe). As a result, the proposed CBLTwitter model of the BERT word embedder in disaster prediction, which consists of an attention-layer Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) architectures, provided performance results competitive with the literature.
Keywords : Dikkat mekanizması, BERT, BiLSTM, CNN, NLP, Twitter verisi

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