Performance Analysis on Detection of Cyberbullying in Code-Mixed Language on Social Media
Keywords:
lower code-mixed languages, natural language processing, social networking, machine learning, cyberbullyingAbstract
Bullying that takes place online is known as cyberbullying. Social media's explosive expansion has made a lot of individuals, particularly young people, more vulnerable to cyberbullying. By using machine learning, we can identify linguistic patterns in the posts that involve cyberbullying and create a model that can automatically identify cyberbullying content. With the rapid growth of social media, cyberbullying has emerged as a major concern, affecting individuals' mental and emotional well-being. The detection and mitigation of cyberbullying are vital for creating safer digital spaces. This survey paper provides a comprehensive review of recent advancements in the detection of cyberbullying on social media platforms. It explores various approaches, including traditional machine learning methods and natural language processing (NLP) techniques. This study aims to explore ground-breaking methods for understanding and automatically detecting occurrences of cyberbullying across various social media platforms, including tweets, comments, and messages.
The survey also delves into the unique challenges associated with detecting cyberbullying, such as handling diverse languages, code-mixed text, and the evolving nature of abusive language. Special attention is given to the detection of cyberbullying in multilingual and code-mixed environments, where standard models may struggle to understand linguistic nuances.
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