Cyberbullying Detection

This article discusses the phenomena of cyberbullying especially among young people. The discussion, based on an interdisciplinary study in the fields of brain studies, child development, psychology, social policy, victimization and Internet studies, probes the troubling phenomenon of cyberbullying which may result in suicide. It is argued that adolescents are more vulnerable than adults because they lack maturity with respect to capacities such as thrill seeking, impulse control, peer pressure, reward sensitivity, cognitive processing, rational decision-making and long-term planning. The article suggests remedies to counter online social ills and argues for responsible cooperation between parents, schools, governments, Non-Governmental Organizations (NGOs) and social networking sites.

With the phenomenal increase in use of Social Networking Service (SNS) and mobile technology, the consequences of cyberbullying have become an epidemic. More than 80% youth use cell phones making them extremely vulnerable to the abuse and one in three young people have been found victims of this problem. There are many different methods of detection cyberbullying behaviour patterns however rarely any focuses on analysis based on association especially in Malay language. Learning and detecting using association is a natural communication phenomenon that can help to identify abusive content from the hidden corpora, which often goes unnoticed. Association helps to identify trends, rules and patterns of the bullies and detects abusive content considering whole sets rather than focusing on single instances. The current work focuses on detection of cyberbullying instances by association analysis using the Apriori Algorithm. It mainly focuses on detecting bullying and aggressive behaviour on Twitter. Over 80 different patterns with high confidence levels were detected that can be successfully implemented for the detection process. The high confidence levels are indicative of the efficiency of association analysis for cyberbully detection in SNS.

The prevalence of social media is expanding step by step y. People of all age group are terribly interested in social networking. Social media connects people from different parts of the world. However, social media may have some side effects such as cyber bullying, which may have negative impacts on the life of people. Research shows that children and teenagers are the main victims of this cyber attack. Through the social media, people share their thoughts and emotions with their friends. There are large numbers of fraud accounts in social media. Cyber bullying is when someone, harass others on social media sites. Some people use it for cyber attack by making negative comments on others post. One way to tackle this problem is to detect those bullying messages and encrypt it. Machine learning techniques make automatic detection of cyber bullying messages. Weka is a powe full machine learning tool which can be used for this purpose. A combination of classification and lexical algorithms can detect whether a message is bullying or not. Cyber bullying is a major problem in society since social media has its presence in all fields of modern man's life.

Cyberbullying, or humiliating and slandering people through Internet, has been recently noticed as a serious social problem disturbing mental health of Internet users. In Japan, to deal with the problem, voluntary members of Parent-Teacher Association (PTA) manually read through the Web to spot cyberbullying entries. To help PTA members in their uphill task we propose a novel method for automatic detection of malicious contents on the Internet. The method is based on a combinatorial approach resembling brute force search algorithms with application to language classification. The method extracts sophisticated patterns from sentences and uses them in classification. We tested the method on actual data containing cyberbullying provided by Human Rights Center. The results show our method outperformed previous methods. It is also more efficient as it requires minimal human effort.