Social Media Addiction Analysis Based on Machine Learning

Abstract

Social media is a necessary component of modern living. Although social media has many advantages and applications, over-usage of it has already led to immediate societal and private problems. It has become clear that social media addiction is a brand-new phenomenon and addiction. A lot of issues in our society and in our daily lives are brought on by excessive usage of social media and online resources. Some people spend a significant amount of their day on social media and ignore or forget about their crucial tasks. Massive social media use contributes to physical and mental disorders. Today, depression affects a large portion of the population worldwide. The internet and social media have the power to affect and alter our emotions, cognitive processes, complete ways of thinking, and regular behavioural attitudes and traits. The major melancholy, anxiety, and dissatisfaction are social networking sites like Twitter, Facebook, Snapchat, and other chat tools that allow us to vent our sentiments. Most of the people are addicted to social media. Our main objective is to find out the number of social media-addicted people. To find out the number of addictions, we collected the data by doing a survey and learned the data in machine learning algorithm and tried to find the number of social addictions through sentimental analysis from that collected data set. Different machine learning algorithms: Decision Tree Classifier, Random Forest Classifier, SVC, and K-Nearest Neighbours have been used.