ALBERT4Spam: Sosyal Ağlarda Spam Tespitinde Yeni Bir Yaklaşım

Year 2024, Volume: 17 Issue: 2, 81 - 94, 30.04.2024

Rezan Bakır Hasan Erbay Halit Bakır

https://doi.org/10.17671/gazibtd.1426230

Abstract

Sosyal medyada gezinmek, insanların katıldığı en popüler çevrimiçi etkinliklerden biridir. Sosyal medya, günlük hayatlarımıza daha fazla entegre oldukça, kurbanlarına sosyal ağ siteleri aracılığıyla ulaşmak isteyen spam göndericilere sayısız fırsat sunmaktadır. Sosyal ağlar üzerinden iletilen mesajlar genellikle kısa ve seyrek olduğu için, kısa metin sınıflandırma problemleri ortaya çıkmaktadır. Bu tür sorunların üstesinden gelmek için, sınıflandırıcının etkinliğini artırmak amacıyla metni uygun şekilde temsil etmek önemlidir. Bu amaçla, bu çalışma, sosyal medya platformlarında spam tanımlamak için derin öğrenme yaklaşımı olan ALBERT4Spam'i tanıtmaktadır. ALBERT modelinden gelen bağlamsal kelime temsilleri kullanılarak önerilen Çift Yönlü Uzun Kısa Süreli Bellek (BLSTM) sinir ağı mimarisinin performansı artırılmıştır. Önerilen ALBERT4Spam modelinde kullanılan BLSTM katman sayısı, nöron sayısı, katman sayısı, aktivasyon fonksiyonu, öğrenme oranı, ağırlık başlangıç, optimizer ve bırakma gibi hiperparametreler, en iyi performansa ulaşmak için rastgele arama yöntemi kullanılarak optimize edilmiştir. Üç farklı standart veri seti üzerinde yapılan deneysel sonuçlar, önerilen modelin mevcut modellere kıyasla sosyal medya platformlarındaki spam mesajlarını daha başarılı bir şekilde tespit ettiğini göstermektedir. Yapılan deneyler, Twitter, YouTube ve SMS veri setlerinde sırasıyla %98, %96 ve %98 kesinlik sonuçlarıyla daha üstün performans sergilediğini ortaya koymaktadır.

Keywords

Spam tespiti, Kelime gömme, Derin öğrenme, BERT, ALBERT, BLSTM

ALBERT4Spam: A Novel Approach for Spam Detection on Social Networks

Abstract

Engaging in social media browsing stands out as one of the most prevalent online activities. As social media increasingly integrates into our daily routines, it opens up numerous opportunities for spammers seeking to target individuals through these platforms. Given the concise and sporadic nature of messages exchanged on social networks, they fall within the realm of short text classification challenges. Effectively addressing such issues requires appropriately representing the text to enhance classifier efficiency.Accordingly, this study utilizes robust representations derived from contextualized models as a component of the feature extraction process within our deep neural network model, which is built upon the Bidirectional Long Short-Term Memory neural network (BLSTM). Introducing ALBERT4Spam, the study presents a deep learning methodology aimed at identifying spam on social networking platforms. It harnesses the proven ALBERT model to acquire contextualized word representations, thereby elevating the effectiveness of the suggested neural network framework.The random search method was used to fine-tune the ALBERT4Spam model's hyperparameters, which included the number of BLSTM layers, neuron count, layer count, activation function, weight initializer, learning rate, optimizer, and dropout, in order to obtain optimal performance. The experiments conducted on three benchmark datasets demonstrate that our innovative model surpasses widely used methods in social network spam detection. The precision results stand at 0.98, 0.96, and 0.98 for Twitter, YouTube, and SMS datasets, respectively, showcasing superior performance outcomes.

Keywords

Spam detection, Word embedding, Deep learning, BERT, ALBERT, BLSTM

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