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

Halit Çetiner; Hakan Yüksel

doi:10.17714/gumusfenbil.1653072

TR EN

CBLTwitter: CNN-BiLSTM derin öğrenme yöntemlerini kullanarak Twitter felaket tespiti

Öz

Sosyal medya platformlarından biri olan Twitter, herkesin düşünce ve fikirlerini çevrimiçi olarak dile getirmesini sağlayan güvenilir kaynaklardan biridir. Bu makalede, Twitter platformundaki tweet içeriklerindeki olası afet ya da felaket gibi olağanüstü durumlardaki metin içeriklerinin incelenmesi ve analizine odaklanılmıştır. Twitter platformundan alınan gerçek zamanlı bilgiler neticesinde olası felaket durumlarında insanlara yardımcı olmak ve acil durum ekiplerini otomatik olarak yönlendirme yapmak mümkündür. Belirtilen olası senaryoları gerçekleştirilmesine zemin hazırlayabilmek için binlerce ham metin içeriği içerisinden felaket ile ilgili içerikleri tespit ederek yüksek performans seviyesine sahip sınıflandırma gerçekleştirmek gerekmektedir. Bu makalede, felaketler hakkında karar verebilen ham tweet bilgileri içerisindeki yerel kalıpları ve bağlamsal bağımlılıkları yakalayabilen önemli değerlerinin ağırlık puanlarını artırarak sınıflandırma yapan CBLTwitter modeli önerilmiştir. Proposed CBLTwitter modeli, Twitter verilerinden felaket tahmin etmede Bidirectional Encoder Representations from Transformers (BERT) adlı bağlamsal kelime yerleştiricinin etkinliğini araştırmaktadır. Bunların yanısıra BERT sonuçlarının, Word2Vec ve Global Vectors for Word Representation (GloVe) adlı bağımsız kelime yerleştirme yöntemlerinden elde edilen sonuçlar ile karşılaştırılması yapılmaktadır. Sonuç olarak felaket tahmininde BERT kelime yerleştiricinin attention katmanlı Convolutional Neural Network (CNN) ve Bidirectional Long Short Term Memory (BiLSTM) mimarilerinden oluşan proposed CBLTwitter modeli literatür ile rekabet edebilir performans sonuçları sağlamıştır.

Anahtar Kelimeler

Dikkat mekanizması, BERT, BiLSTM, CNN, NLP, Twitter verisi

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

Öz

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.

Anahtar Kelimeler

Attention mechanism, BERT, BiLSTM, CNN, NLP, Twitter data

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Doğal Dil İşleme

Bölüm

Araştırma Makalesi

Yazarlar

Halit Çetiner ^*
0000-0001-7794-2555
Türkiye

Hakan Yüksel
0000-0003-2186-533X
Türkiye

Yayımlanma Tarihi

15 Haziran 2025

Gönderilme Tarihi

7 Mart 2025

Kabul Tarihi

15 Mayıs 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 15 Sayı: 2

DOI

https://doi.org/10.17714/gumusfenbil.1653072

IZ

https://izlik.org/JA78KD39PW

APA

Çetiner, H., & Yüksel, H. (2025). CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 15(2), 563-576. https://doi.org/10.17714/gumusfenbil.1653072

AMA

1.Çetiner H, Yüksel H. CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2025;15(2):563-576. doi:10.17714/gumusfenbil.1653072

Chicago

Çetiner, Halit, ve Hakan Yüksel. 2025. “CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 15 (2): 563-76. https://doi.org/10.17714/gumusfenbil.1653072.

EndNote

Çetiner H, Yüksel H (01 Haziran 2025) CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 15 2 563–576.

IEEE

[1]H. Çetiner ve H. Yüksel, “CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods”, Gümüşhane Üniversitesi Fen Bilimleri Dergisi, c. 15, sy 2, ss. 563–576, Haz. 2025, doi: 10.17714/gumusfenbil.1653072.

ISNAD

Çetiner, Halit - Yüksel, Hakan. “CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 15/2 (01 Haziran 2025): 563-576. https://doi.org/10.17714/gumusfenbil.1653072.

JAMA

1.Çetiner H, Yüksel H. CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 2025;15:563–576.

MLA

Çetiner, Halit, ve Hakan Yüksel. “CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods”. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, c. 15, sy 2, Haziran 2025, ss. 563-76, doi:10.17714/gumusfenbil.1653072.

Vancouver

1.Halit Çetiner, Hakan Yüksel. CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi. 01 Haziran 2025;15(2):563-76. doi:10.17714/gumusfenbil.1653072

Cited By

Adaptive Ensemble Learning with a Fine-Tuned Framework for Cyberbullying Detection in Cross-Platform Social Media Environments

Engineering, Technology & Applied Science Research

https://doi.org/10.48084/etasr.15164

e-ISSN: 2146-538X Yayın Modeli: Sürekli Yayın Atıf Dizinleri: TR Dizin , SCOPUS

CBLTwitter: CNN-BiLSTM derin öğrenme yöntemlerini kullanarak Twitter felaket tespiti

Öz

Anahtar Kelimeler

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Adaptive Ensemble Learning with a Fine-Tuned Framework for Cyberbullying Detection in Cross-Platform Social Media Environments