Margono, Hendro (2019) Analysis of the Indonesian Cyberbullying through Data Mining: The Effective Identification of Cyberbullying through Characteristics of Messages. PhD thesis, Victoria University.

Abstract

The use of social networks sites such as Facebook, Twitter, YouTube, Instagram, and LinkedIn has increased rapidly in the last decade. It has been pointed out in the international data that more than 83% of people between the age of 18 and 29 have used social networking sites (Best et al., 2014). Social networks are also a powerful medium that can be used for positive purposes, such as communication and information sharing, and can provide easy access to fresh news. On the other hand, social network sites can be used for negative purposes such as harassment and bullying. Bullying on social networks is usually called cyberbullying. Cyberbullying has emerged as a significant issue and become an important topic in social network analysis, as more than 10% of parents globally have stated that their child has been cyberbullied (Gottfried, 2012). Ipsos reported that in Indonesia 91% of parents stated their children were bullied on social media in 2012 (Gottfried, 2012). Moreover, 58% of Indonesian adolescents ranging in age from 12 to 21 reported that they often suffered online harassment and humiliation (Dipa, 2016). Therefore, to be able to understand this phenomenon, the use of machine learning methods in data mining techniques can potentially assist in analysing cyberbullying issues. However, there are several points to be taken into consideration in the rapid use of various vocabularies for cyberbullying, the patterns of harmful words used in cyberbullying messages, and the scale of the data. The purpose of this research is to identify the indicators of cyberbullying within the written content, and to propose and develop effective models of analysis with the goal of detecting the incidence of cyberbullying activities on social networks. Therefore, this research has addressed concerns about the measurement of cyberbullying and aimed to develop a reliable and valid measurable tool. Through developing systematic measurement and techniques, this research has enhanced an effective analysis model to discover the patterns of insulting words which can assist in accurately detecting cyberbullying messages. The research in this thesis has developed the analysis model using association rules and classification techniques. These techniques have been used for effective identification of cyberbullying messages on social networks. Furthermore, this research has discovered interesting patterns of insulting words which can assist in identifying cyberbullying messages. The experimental results have also indicated that the proposed method can predict the messages precisely into cyberbullying or non-cyberbullying. Moreover, 80.37% of the total data has been detected as cyberbullying. Overall, this thesis makes a significant contribution in identifying new characteristics for cyberbullying recognition, in developing the analysis method for social issues and in advancing the parameters to determine the strength of the relationship between data in relation to data mining techniques. The research in this thesis presents the analysis results and contributes to our understanding of various cyberbullying patterns. Also, the results can be developed further in future research.

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