Leveraging Machine Learning and Behavioural Analytics to Identify Gaming Dependency and Online Toxicity Among Indian Youth

Abstract

The digital gaming market in India has been rapidly developing as a multi-billion rupee industry due to the participation of youth in this field and the engulfing technology. However, under its financial potential, there is a wave of behavioural addicts and digital toxicity. The topic of the paper is to investigate how machine learning and behavioural analytics can be useful in identifying and understanding the trends in gaming addiction and toxic behaviour online among Indian adolescents. The present study conducts a study using predictive modelling, sentiment analysis, and correlation mapping, utilising secondary data through the Internet and Mobile Association of India (2024), the World Health Organisation (2023), and the National Crime Records Bureau (2023) and analysing the behavioural patterns related to compulsive gaming. The results indicate that adolescents who spend over six hours a day in a gaming space have a 3.4-fold increased risk of psychological addiction and are also twice as prone to having toxic online interactions. This paper suggests an ethical human-centred AI intervention model that could identify early warning symptoms of behaviour modification without violating privacy. The paper arrives at the conclusion that the digital ecosystem of the young Indian population should not be based on the principles of algorithmic engagement but on the concept of emotional balance, in which machine learning is used not to increase dependence but to recover it.