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Paper Details

A Machine Learning Approach To Predict Social Media Addiction During COVID-19 Pandemic
Meherin Akter, Kaniz Fatima Ritu, Md. Sadekur Rahman, Md. Tarek Habib,

The world is now in an extremely precarious situation due to the COVID-19 pandemic. People devote a lot of time to social media sites these days. Just as social media has stood by people during this pandemic, it has also caused trouble in some cases. Excessive use of social media harms mental as well as physical well-being. In our research project, the use of social media by Bangladeshi people throughout the year 2021 has been examined to anticipate their level of addiction in this COVID-19 circumstance. The data has been gathered from people of various age ranges, occupations, and the levels of addiction have been analyzed. Using several methods and machine learning classifiers, their addiction to social media has been predicted in which the levels are categorized into four labels. Different feature selection techniques and machine learning classifiers have been employed and found the maximum accuracy, 94.05% in logistic regression.

COVID-19 , Support vector machines , Social networking (online) , Pandemics , Feature extraction , Data mining , Regression tree analysis
Journal or Conference Name
2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
Publication Year