Una lectura general

As discussed in Chapter 2, cyberbullying can be defined as the repetitive acts of intimidation and aggressive harassment by an individual or group with the intention of intimidating or harming a targeted person by means of insulting text messages, pictures and videos sent via communication technologies (Smith et al., 2008; UNDP, 2013). This indicates that cyberbullying does not solely involve one or more verbal attacks; it involves a range of electronic media used to send possibly destructive messages to the targeted victims, while the perpetrators whether as an individual or

172 with the assistance of friends and followers perform the constant cyberbullying action within a certain period of time.

By understanding the characteristics and forms of cyberbullying messages, positive or negative messages can be identified through the characteristics of the contents of messages (Sanders et al., 2009). This means that in order to determine whether or not a message is an instance of cyberbullying, it is important to know, understand and recognize the characteristics of messages because every sentence in a message contains a combination of words that conveys either a positive or negative sentiment.

Willard (2006) created various classifications for cyberbullying messages. These include: flaming, harassment, denigration, impersonation, outing and trickery, and cyberstalking. Cyberbullying is characterised by the anonymity of the perpetrator (O’Brien and Moules, 2010); the timing for sending and re-sending harmful messages (Privitera and Campbell, 2009); the intentionality of re-sending messages (Spears et al., 2009); and the actual content of messages (Mishna et al., 2009).

The characteristics and form of cyberbullying explained above provide a rich opportunity to examine common findings and emerging models concerning the identification of other characteristics of cyberbullying messages. This research suggests that there is another method that can be used to recognize the characteristics of cyberbullying messages - that is, detecting the pattern of insulting words that frequently appear in social network messages. The recognition of patterns of insulting words is another important means of discovering whether messages contain cyberbullying.

This research explains the process of finding the pattern of insulting words using data mining techniques which helps to detect the characteristics of cyberbullying

173 messages. Figure 1 below shows the cyberbullying message characteristics mind map expanded with the findings of the research in this thesis of insulting word patterns.

In this case, the findings in this research show that the patterns of Indonesian insulting words can be used to detect cyberbullying messages from Twitter. Three general principles can be applied in the identification of Indonesian cyberbullying messages. First, the patterns of Indonesian insulting words can be used, but there are constraints. That is, the patterns of Indonesian insulting words can be used to identify cyberbullying in the Indonesian context, but cannot be used to identify cyberbullying messages conveyed in other languages. Second, the patterns of Indonesian insulting words have undergone a measurement process to determine the strength of the relationship between insulting words. Third, the identification of the cyberbullying messages can be investigated empirically by observation and experience in finding the patterns of the insulting words that often occur in cyberbullying messages.

Cyberbullying Messages

Anonymity (O’Brien and Moules, 2010)

Place and Time (Privitera and Campbell, 2009) Power differentials (Spears et al., 2009) Spreading rumor (Mishna et al., 2009) The insulting word patterns (results of the research of this thesis)

174 A combination of hierarchical assigning tasks and structured mapping tasks was used by applying association rules techniques of FP-growth and cosine similarity in order to find the data patterns. In addition, a combination of naïve Bayes, decision tree and neural network are used to detect the classification of data labels. These combinations were effective as it produced relevant results and was more time- efficient compared to a manual cyberbullying identification method. The findings presented in Chapters 3 and 4, among other things, illustrate a distinctive difference between those of previous research and this research. First, in previous research the cyberbullying messages were identified through the anonymity of the perpetrator (O’Brien and Moules, 2010); the timing for sending and re-sending harmful messages (Privitera and Campbell, 2009); the intentionality of re-sending messages (Spears et al., 2009); and the actual content of messages (Mishna et al., 2009). However, this research has successfully identified cyberbullying messages by examining the patterns of insulting words. Therefore, the patterns of insulting words can also be another means of identifying cyberbullying messages. Second, the outcomes of experiments have shown that the results obtained from the structural combination of using data mining techniques are highly accurate in identifying cyberbullying messages. The reason for this is that a well-design analysis model developed by the author in this research resulted in high accuracy in data prediction through extended parameter instruments such as confidence, laplace, conviction including accuracy, precision and recall. The result of high accuracy in data prediction was explained in Chapter 4.

The structured mapping task of the analysis model was explicitly designed in this research in such a way that the author can easily recognise and develop the identification process of an analysis model in future work. Therefore, to conclude this

175 section, the results obtained by the identification process indicate a high level of accuracy in the identification of cyberbullying messages. Also, the patterns of insulting words that have been found in this research contribute to the detection of cyberbullying words in message contents.