Nuha Almozaini, Yasmin Alateeq, Noura Alrajeh and Saleh Albahli, Qassim University, KSA

Sharing information has been democratized with the rise of social networks. Consequently, increasing the usage of Social Network, especially Twitter platform, leads to growing malicious activities. With a silent hack, a hacker can continuously dig around to control over victim’s account. In this paper, an observed direct impact to users’ security and privacy has been identified. Therefore, we address hidden tactics in the problem specific feature engineering with detailed results to show how deep leaning classifiers are promising direction to understand sentiment than classical machine learning. Thus, we focus on the state of the art Deep learning techniques by constructing a model to detect behavioral changes of users. Our evaluation shows that working with just classical machine algorithms to analyse social data do not achieve higher performance than deep learning algorithms. This will open directions for using deep learning for similar problems. Moreover, our results demonstrate the shortages of classical Machine Learning classifiers compared to Deep learning and how they can be mitigated.

Deep learning, Machine learning, silent hacking, social data, behaviors, analysis, Twitter.