Abstract
Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naive Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naive Bayes, artificial immune systems, support vector machines, etc.
Highlights
Since the main interest of the paper is the proposal of a social network spam filter based on deep neural network with ensemble learning, we provide a brief description of these methods
FN rate represents the percentage of spam messages incorrectly predicted as legitimate, while FP rate is the percentage of legitimate messages incorrectly predicted as spam
We demonstrated that ensemble learning algorithms with deep neural network (DNN) as the base learner is more accurate than state-of-the-art spam filtering methods
Summary
Spam can be defined as unwanted and unsolicited messages sent to usually a large number of recipients [1]. Spam message can be sent over multiple communication channels, such as e-mail, SMS, social networks, etc. Statistics show that a large proportion of all messages in social networks are spam messages. The growing opportunities of social networks and their popularity have attracted many users. Spam senders target social network users as well. Business social networks like Linkedin are affected [3]. This has serious economic and social consequences. Spam messages decrease work productivity, increase IT support related resources (help desk) and may even result in security incidents. This is why a considerable attention is given to spam filtering
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