Cyberbullying is characterised by “intentional, repeated acts of sending aggressive or harmful messages online to a victim with the intent to harass, ridicule or mistreat the target”.
Due to the nature of online communities (OCs), members interact with each other within boundaries, creating their own personalities for the nicknames they use through the interactions. They obtain new information on common interests and gain a sense of belonging. Organised cyberbullying is committed in an OC by a coordinated group of members who commit different levels of harassment against the victim aiming his/her voluntary exit from the OC. It can be better understood from group and process dynamics perspective as it is not just a dyadic relationship between an attacker and a victim, but the bullying behaviours are developed by multiple actors over a meaningful period.
The literature on cyberbullying lacks studies on the group and process dynamics of organised cyberbullying behaviours in OCs. Scholars have studied the phenomena related to cyberbullying since the early 2010s. Existing studies have identified characteristics and protective factors of cyberbullying behaviours. The extant literature mainly focuses on understanding cyberbullying phenomena in virtual spaces including the concepts and definitions, characteristics and behaviours of participants (perpetrators, victims, and bystanders), and methods for prevention and detection of cyberbullying.
Positivism and interpretivism which are the major approaches taken by existing studies for understanding causal relationships among related variables are not appropriate to answer the questions as evolutionary aspects of such behaviours require a process-oriented approach.
To overcome shortcomings in relation with evolutionary changes and open systems, we investigate the feasibility of adopting a computational social science approach based on critical realism principle. In a process-oriented approach, event data is important to understand how members’ activities (footprints) unfold and change as time goes.
However, in OC context, collecting whole footage data of members is usually difficult as we are limited to see only part of actual activities of members while a majority of activities are hidden in OC platforms. For example, in OCs, we can trace a member’s activities only when the member posts an article or make a comment while we usually do not know how often the member visits the OC and reads how many articles. Such activities are stored in a database of an OC platform but not seen by other members. Due to such physical and structural limitations, it is difficult to observe all footages of members in an OC and it is difficult to explain why and how cyberbullying behaviours unfold in OCs.
Critical realism provides researchers with guidelines to identify a generative mechanism (causal powers) for observed empirical data by dividing ontologies into three domains: empirical, actual, and real. The real domain includes entities, structures, and the causal powers inherent in them. Events are created when the causal powers of entities or structures are enacted. The events created by causal powers may be different when the causal powers interact with the contextual factors of the settings. The domain of the actual is a subset of the domain of the real and includes events created by causal powers. These are events that have occurred, whether they are observed by humans or not. The domain of the empirical is a subset of the domain of the actual and includes events that are experienced by human observers. In OC context, identifying the generative mechanism (causal power) of organised cyberbullying in real domain requires processing a large amount of digital footprints data collected in an empirical domain.
For OC managers, detecting organised cyberbullying behaviours in OCs is more difficult in the early stage, although it is critical in order to prevent any negative legal, economic and sociological impacts. For effective prevention and management of organised cyberbullying in OCs, OC managers need to understand the dynamics of organised cyberbullying behaviours that include what are the precautious signal of cyberbullying, what patterns of interactions among participants occur, and how the interaction patterns change over time.