Cyberbullying Detection using Machine Learning Algorithms

Abstract

Abstract: In today's digital age, the internet has become an integral part of our lives, resulting in an exponential increase in the volume of data generated. However, this surge in online activity has also led to the emergence of cyberbullying as a major concern in web 4.0. Cyberbullying refers to the use of technology to intimidate, harass or threaten an individual, and is considered a form of cybercrime. Given the lack of available datasets, anonymous identities of perpetrators and the privacy of victims, previous research in cyberbullying detection has been limited. To address this issue, a new approach based on text mining and machine learning algorithms is proposed to proactively detect bullying text. Unlike previous research, which only considered textual features, the current study extracts three types of features: textual, behavioural and demographic features. Textual features include specific words commonly used in cyberbullying, which may indicate the presence of bullying behaviour. Behavioural features are based on personality traits and are extracted to determine the likelihood of a user engaging in bullying behaviour in the future. Demographic features, such as age, gender and location, are also extracted from the dataset. Overall, this text mining approach using machine learning algorithms can effectively detect cyberbullying, providing a valuable tool to combat this growing concern in the cyber world.