Abstract
The current period is dominated by social networks in daily life. Despite the several efforts and research practices done to overcome the issue, it still presents a problem. Despite the fact that social networks are useful for social gathering and communication, they also present new opportunities for harmful criminal acts. Cyber-harassment is an example that is enabled through the mistreatment and abuse of the internet as a means of harassing or bullying others virtually. To minimize these occurrences, research into computer-based methods has been per-formed to detect cyber-harassment. This literature survey shows that supervised learning methods were mostly used for cyber-bullying detection. Moreover, some non-supervised methods and other techniques have also shown to be effective in terms of accuracy towards cyber-bullying detection. This paper, therefore, surveys existing recent research on non-supervised techniques as well as it summarizes accuracy results obtained from several papers to discuss the significance of non-supervised learning approaches in comparison with traditional learning methods.
Highlights
In 2011, Jamey Rodemayer [1], an American adolescent aged fourteen, killed himself because he has faced cyberbullying for years due to his sexuality
They analyzed questions from each bully in each conversation, modeling it as “multivariate time series.”. They changed their scheme of representation from time series data to symbolic string representation in their later work [14]. They were influenced by the “Multiple Sequence Alignment” (MSA) method which is widely used in the identification of DNA Series
Papers have emphasized on supervised learning techniques, and very few have tested non supervised approaches which leaves more room for research, and more room for harassers to manipulate victims
Summary
In 2011, Jamey Rodemayer [1], an American adolescent aged fourteen, killed himself because he has faced cyberbullying for years due to his sexuality. For all the measures to control or restrict cyber-bullying prevalence, internet abusers may find a subtle way to remove that regulation Another issue that faces the scientific society is that high quality features of data sets are lacking. It serves to make clear that all the steps to remove or reduce it issue still require more effort, as the internet leaves plenty of space for cyberbullying to take place. These issues have inspired scientists and researchers to explore tools and techniques that were not supervised, including semi-supervised, deep learning & unsupervised learning techniques. Section five gives a summary of the results and the conclusion is given in the final section
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