Context-dependent model for spam detection on social networks
Yükleniyor...
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER INTERNATIONAL PUBLISHING AG
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.
Açıklama
Anahtar Kelimeler
Spam detection, Word embedding, Bidirectional encoder representations from transformers
Kaynak
SN APPLIED SCIENCES
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
2
Sayı
9
Künye
Ghanem, R., & Erbay, H. (2020). Context-dependent model for spam detection on social networks. SN Applied Sciences, 2(9), 1-8.
