Contents-Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTM

• Published in: SN computer science Vol. 4; no. 4; p. 380
• Main Authors: Ghanem, Razan, Erbay, Hasan, Bakour, Khaled
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Blacklisting; Comparative studies; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Deep learning; Information Systems and Communication Service; Language; Messages; Methods; Natural language processing; Neural networks; Original Research; Pattern Recognition and Graphics; Recurrent neural networks; Representations; Social networks; Software Engineering/Programming and Operating Systems; Sparsity; Support vector machines; Vision; Stacked BLSTM; Word embedding; BERT; Transformer; RoBERTa; Spam detection

The use of social networks has become an integral part of our daily lives. Even though social networking sites offer many advantages, they also pose a number of problems for their users. One of the most famous problems is unwanted messages. It is not desirable for social network users to be bothered...