Cyberbullying Detection in Hinglish Language

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing.