Cyberbullying Detection using Time Series Modeling

Cyber bullying is a new phenomenon resulting from the advance of new communication technologies including the Internet, cell phones and Personal Digital Assistants. It is a challenging bullying problem occurring in a new territory. Online bullying can be particularly damaging and upsetting because it´s usually anonymous or hard to trace. In this paper, the proposed method is utilizing a dataset of real world conversations (i.e. Pairs of questions and answers between cyber predator and the victim), in which each predator question is manually annotated in terms of severity using a numeric label. We approach the issue as a sequential data modelling approach, in which the predator´s questions are formulated using a Singular Value Decomposition representation. The motivation of this procedure is to study the accuracy of predicting the level of cyber bullying attack using classification methods and also to examine potential patterns between the lingustic style of each predator. More specifically, unlike previous approaches that consider a fixed window of a cyber-predator´s questions within a dialogue, we exploit the whole question set and model it as a signal, whose magnitude depends on the degree of bullying content. Using feature weighting and dimensionality reduction techniques, each signal is straightforwardly parsed by a neural network that forecasts the level of insult within a question given a window between two and three previous questions. Throughout the time series modeling experiments, an interesting discovery was made. By applying SVD on the time series data and taking into account the second dimension (since the first is usually modeling trivial dependencies between instances and attributes) we observed that its plot was very similar to the plot of the class attribute. By applying a Dynamic Time Warping algorithm, the similarity of the aforementioned signals was proved to exist, providing an immediate indicator for the severity of cyber bullying within a - iven dialogue.