Performance Comparison for Spam Detection in Social Media Using Deep Learning Algorithms

Abstract

People are connected through Twitter or Facebook or any other social medial tool. However, this resulted in messages with malicious content and malware links. Therefore, it's required to own a powerful spam detection design that might stop these styles of messages. Spam detection in hissing platform like Twitter remains a tangle, thanks to short text and high variability within the language utilized in social media. In this paper, we tend to propose a CNN algorithmic technique and compare results with variants of CNN and with boosting algorithms. The model is supported with the assistance of knowledge-bases such as Word2vec and fastText. The use of these knowledge-bases improves the performance, by providing higher linguistics vector illustration of input testing words. Projected Experimental results with input datasets show the effectiveness of the proposed model in terms of accuracy and F1 Score.