Abstract. Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of "bulliness" of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance. With the growth of the use of Internet as a social medium, a new form of bullying has emerged, called cyberbullying. Cyberbullying is defined as an aggressive, intentional act carried out by a group or individual, using electronic forms of contact repeatedly and over time against a victim who cannot easily defend him or herself [1] . One of the most common forms is the posting of hateful comments about someone in social networks. Many social studies have been conducted to provide support and training for adults and teenagers [2, 3] . The majority of the existing technical studies on cyberbullying have concentrated on the detection of bullying or harassing comments [4-6], while there is hardly work on the more challenging task of detecting cyberbullies and studies for this area of research are largely missing. There are few exceptions however, that point out an interesting direction for the incorporation of user information in detecting offensive contents, but more advanced user information or personal characteristics such as writing style or possible network activities has not been included in these studies [7, 8] . Cyberbullying prevention based on user profiles was addressed for the first time in our latest study in which an expert system was developed that assigns scores to social network users to indicate their level of 'bulliness' and their potential for future misbehaviour based on the history of their activities [9] . In the previous work we did not investigate machine learning models. In this study we focus again on the detection of bully users in online social networks but now we look into the efficiency of both expert systems and machine learning models for identifying the potential bully users. We compare the performance of both systems for the task of assigning a score to social network users that indicates their level of bulliness. We demonstrate that the expert system outperforms the machine learner and can be effectively combined in a hybrid classifier. The approach we propose can be used for building monitoring tools to stop potential bullies from conducting further harm. In this section we will explain the characteristics of the corpus used in this study. We also describe the feature space and the three feature categories that have been used for the design of the expert system and for the machine learning models. YouTube is a popular user-generated content video platform. Its audience demographics match those from the general internet population [10] . Unfortunately, bullies misuse the platform to victimize their targets through harassing comments and other misbehaviours. To our knowledge no dataset for cyberbully detection is publicly available and therefore we decided to collect our own. To have a reasonable number and variety of cyberbullies we searched YouTube for topics sensitive to cyberbullying. We determined the users who commented on the top three YouTube videos in each topic. A total of 3,825 users were extracted and we collected a log of the users' activities for a period of 4 months (April -June 2012). We also captured profile information of the users, such as their age and the date they signed up. In total there are 54,050 comments in our dataset. On average there are 15 comments per user (StDev = 10.7, Median = 14). The average age of the users is 24 with 1.5 years of membership duration. Two graduate students were employed to independently annotate the users as bullies or non-bullies (inter-annotator agreement = 93%, Kappa = 0.78) based on their comments and the definition of cyberbullying provided earlier. The users that both annotators had labelled as bullying (n=419) were marked as bullies. Disagreements were resolved by the decision of a third annotator. In total, 12% of the users were labelled as bullies. Feature space We compiled a set of 11 features in three categories to be used in our models. The selection of the features was limited by what is technically possible to extract from YouTube. The three categories representing the actions, behaviour, and characteristics of the users are presented in Table 1 . In this section we introduce the three types of models used for calculating and assigning the bulliness score to the social network users: a multi-criteria evaluation system, a set of machine learning models and two hybrid models that combine the two. 3
REF used expert systems to classify between bullying and no bullying in posts.
Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies
Canadian Conference on AI
Computer Science