Analysing Lesbian, Gay, Bisexual, Transgender and Queer or Questioning (LQBTQ) Cyberbullying Using Unsupervised Associative Approach Text Analytics Technique

Abstract

Cyberbullying has become one of the major threats in our society today due to the massive damage that it can cause not only in the cyber world and the internet-based business but also in the lives of many people. The sole purpose of cyberbullying is to hurt and humiliate someone by posting and sending threats online. However, recognition of cyberbullying has proved to be a hard and challenging task for information technologists. The main objective of this study is to analyze and decode the ambiguity of human language used in cyberbullying Lesbian, Gay, Bisexual, Transgender and Queer or Questioning (LGBTQ) victims and detect patterns and trends from the results to produce meaning and knowledge. This study will utilize an unsupervised associative approach text analysis technique that will be used to extract the relevant information from the unstructured text of cyberbullying messages. Furthermore, cyberbullying incidence patterns will be analyzed based on recognizing relationships and meaning between cyberbullying keywords with other words to generate knowledge discovery. “Fuck” and “Shit” account almost half of all cyberbullying words and appear more that 75 % in the dataset as the most frequently used words. Further, the terms “shit”+“hate”+ “fuck” with a positive lift value and “shit”+ “stupid” positive obtained the highest chance of togetherness / chance of utilizing both of these words to cyber bully. The combination of words / word patterns was considered abusive swearing is always considered rude when it is used to intimidate or humiliate someone. The output and results of this study will contribute to formulating future intervention to combat cyberbullying. Furthermore, the results can be utilized as a model in the development of a cyberbullying detection application based on the text relations / associations of words in the comments, replies, blog discussion and discussion groups across the social networks.