No abstract

Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.INDEX TERMS Big data, cyberbullying, cybercrime, human aggressive behavior, machine learning, online social network, social media, text classification.

Sensor nodes heterogeneity if not properly utilized could lead to uneven energy consumption and load imbalanced across the network, which degrades the performance of the network. Routing algorithms should try to achieve energy-efficiency and load-balancing among the heterogeneous nodes to prolong network lifetime. One of the solutions is by using duty-cycling in cluster-based routing such as in Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm to minimize redundant transmission to achieve energy efficiency. However, this scheme suffers from idle listening problem, which lead to energy wastage across the network. Moreover, SEED cannot cope with an environment with sensor nodes with heterogeneous traffic rate. To cope with energy and traffic heterogeneity issues among sensor nodes, a traffic and energy aware routing protocol (TEAR) is proposed. TEAR avoids selecting node with low energy and high traffic rate for cluster head role to achieve load balancing. However, TEAR does not avoid redundant transmission from the sensor nodes that are in close distances. In this paper, we proposed a hybrid method called energy and traffic aware sleep-awake (ETASA) mechanism to improve energy efficiency and enhanced load balancing in heterogeneous wireless sensor network scenario. Unlike prior methods, in ETASA, the paired nodes alternate into sleep and awake mode based on node's energy and traffic rate. Moreover, we revised the conventional TDMA scheduling in SEED by allocating one slot for group of pairs in a cluster. This is done to address idle listening problem to minimize energy consumption. The proposed method improves the cluster head selection technique that selects high energy, low traffic and nodes with high number of pairs to improve balanced energy consumption. The proposed approach is evaluated and compared against the state-of-the-art baseline protocols. The result shows that the proposed ETASA has 16% and 15% lifetime improvements against TEAR and SEED.