TRI-EMBEDDINGS: A NOVEL APPROACH FOR DETECTING ABUSIVE LANGUAGE ON SOCIAL MEDIA

Rezan Bakır

doi:10.17780/ksujes.1679361

TR EN

TRI-EMBEDDINGS: SOSYAL MEDYADA SALDIRGAN DİL TESPİTİ İÇİN YENİ BİR YAKLAŞIM

Öz

Küresel dijital iletişimin artışı, nefret söylemi ve uygunsuz dil içeren metinlerin yayılmasına yol açmış ve bu durum toplumsal refahı tehdit etmektedir. Yapay zekâ, siber güvenlik zorlukları yaratırken, aynı zamanda bu sorunların çözülmesinde de kritik bir rol oynamaktadır. Araştırmacılar, algoritmaların yanlış kullanımını azaltmak ve siber güvenliği sağlamak için çok disiplinli bir yaklaşım benimsemelidir. Bu çalışmada, Twitter verisi üzerinde yapay zekâ destekli metin analiziyle uygunsuz kelimeler içeren metni tespit etmek amacıyla Tri-Gömülü Temsiller (Tri-Embeddings) adı verilen yenilikçi bir yöntem sunulmaktadır. Bu yöntem, Word2Vec, FastText ve Universal Sentence Encoder (USE) gibi önceden eğitilmiş modelleri birleştirir. Semantik incelikleri yakalamak ve modelin uygunsuz kelimeler içeren metindeki ince farkları tespit etme yeteneğini artırmak için yapılan bu entegrasyon, doğruluk ve sağlamlık açısından önemli gelişmeler sağlamıştır. Ek olarak, DistilBERT gibi büyük bir dil modelinin önerilen yönteme dâhil edilmesinin etkisi de incelenmiştir. Bulgular, yüksek doğruluk, duyarlılık ve F1 skoru değerleriyle yöntemin etkinliğini göstermektedir. Bu yaklaşım, etik ihlalleri azaltarak daha güvenli ve kapsayıcı bir çevrimiçi ortam yaratmaya yardımcı olmaktadır.

Anahtar Kelimeler

TRI-EMBEDDINGS: A NOVEL APPROACH FOR DETECTING ABUSIVE LANGUAGE ON SOCIAL MEDIA

Abstract

The rise in global digital communication has led to an increase in hate speech and offensive language, posing significant threats to societal well-being. While AI presents cybersecurity challenges, it also plays a crucial role in addressing these issues. Researchers must develop AI with a multidisciplinary approach, mitigating algorithmic misuse and ensuring cybersecurity. This study introduces Tri-Embeddings, an innovative method for detecting abusive language using AI-powered text analysis, applied to Twitter data. The method combines pre-trained models such as Word2Vec, FastText, and Universal Sentence Encoder (USE). Additionally, this study explores the impact of integrating a large language model, DistilBERT, into the proposed unified embedding framework. The findings demonstrate the method’s effectiveness, with high precision, recall, and F1 scores, showing its potential to reduce the spread of offensive and hateful language. This approach helps mitigate ethical breaches and creates a safer, more inclusive online space.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Doğal Dil İşleme

Bölüm

Araştırma Makalesi

Yazarlar

Rezan Bakır ^*
0000-0002-4373-2231
Türkiye

Yayımlanma Tarihi

3 Mart 2026

Gönderilme Tarihi

18 Nisan 2025

Kabul Tarihi

5 Aralık 2025

Yayımlandığı Sayı

Yıl 2026 Cilt: 29 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1679361

IZ

https://izlik.org/JA76LG63HW

Kaynak Göster

RIS / Bibtex

APA

Bakır, R. (2026). TRI-EMBEDDINGS: A NOVEL APPROACH FOR DETECTING ABUSIVE LANGUAGE ON SOCIAL MEDIA. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 278-294. https://doi.org/10.17780/ksujes.1679361