Adrian Smales

Machine learning and semantic analysis of in-game chat for cyberbullying

Abstract One major problem with cyberbullying research is the lack of data, since researchers are traditionally forced to rely on survey data where victims and perpetrators self-report their impressions. In this paper, an automatic data collection system is presented that continuously collects in-game chat data from one of the most popular online multiplayer games: World of Tanks. The data were collected and combined with other information about the players from available online data services. It presents a scoring scheme to enable identification of cyberbullying based on current research. Classification of the collected data was carried out using simple feature detection with SQL database queries and compared to classification from AI-based sentiment text analysis services that have recently become available and further against manually classified data using a custom-built classification client built for this paper. The simple SQL classification proved to be quite useful at identifying some features of toxic chat such as the use of bad language or racist sentiments, however the classification by the more sophisticated online sentiment analysis services proved to be disappointing. The results were then examined for insights into cyberbullying within this game and it was shown that it should be possible to reduce cyberbullying within the World of Tanks game by a significant factor by simply freezing the players ability to communicate through the in-game chat function for a short period after the player is killed within a match. It was also shown that very new players are much less likely to engage in cyberbullying, suggesting that it may be a learned behaviour from other players.

Towards Trust and Governance in Integrated Health and Social Care Platforms

The way health and care data is exchanged will considerably change due to the progressive deployment of integrated care platforms and cyber-physical systems. Next generation network technologies such as 5G will allow for virtualization technologies such as SDN and NFV, which will enable not only data exchange but also in particular multi domain orchestration and service aggregation in real time. This will require “trust” to be embedded into new e-Health platforms as a core value. For the use in M2M systems this needs to be translated in a system of rules and policies, which have to go hand in hand with cutting edge cybersecurity practices such as cryptography and access control. However, there will also be the need for strong governance especially when health information will become accessible across different domains and networks. In this context, specific translation gateways have proven helpful and should be investigated further.