Time Series Forecasting in Cyberbullying Data

Abstract

The present article deals with sexual cyberbullying, a serious subject that has gained significant attention throughout recent years of emerging social media platforms. The detection of sexual predation is one of the most important and challenging tasks in our days. Using real-world data, we follow a time series modeling approach, in which predator’s posts (i.e. questions) are associated with numeric labels, according to the style of the attack (e.g. attempts for physical approach, grooming, retrieval of personal information, etc.). Upon modeling the domain as time series, in order to allow for forecasting the severity of a future question of a predator (i.e. the class label), a sliding window method was adopted. Two well-known methods that have been traditionally applied in time series problems, namely Support Vector Machines and Neural Networks, were utilized for forecasting. Simultaneously, since text processing is almost certain to derive a large number of input features, an additional method for reducing dimensionality of the original dataset was applied, implemented with Singular Value Decomposition. We demonstrate that the use of SVM classifier is more appropriate for our data and we show that it is able to provide accurate results that surpass current state-of-the-art outcomes, by using both the original feature set as well as the reduced SVD dimensions.