Detection and Prevention of Bullying on Online Social Networks: The Combination of Textual, Visual and Cognitive

Abstract

The adoption of online social platforms as a common space for the virtualisation of identities is also correlated with the replication of real-world social hazards in the virtual world. Bullying, or cyberbullying, is a very common practice among people nowadays, becoming much more present due to the increase of online time, especially in online social networks, and having more serious consequences among younger audiences. Related work includes the analysis and classification of textual characteristics that can be indicative of a bullying situation and even a visual analysis approach through the adoption of image recognition techniques. While agreeing that the combination of textual and visual analysis can help the identification of bullying practice, or the identification of bullies, we also believe that a part is missing. In this work, we propose a combination of textual and visual classification techniques, associated with a cognitive aspect that can help to identify possible bullies. Based on a previous model definition for a virtual social sensor, we propose the analysis of textual content present on online social networks, check the presence of people in multimedia content, and identification of the stakeholders on a possible bullying situation by identifying cognitive characteristics and similarities on the behaviours of possible bullies and/or victims. This identification of possible bullying scenario can help to address them before they occur or reach unmanageable proportions.