Browsing by Author "Mim, Minjun Nahar"

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A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms
(Scopus, 2024-10) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
: Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
A Study on Social Media Addiction Analysis on the People of Bangladesh Using Machine Learning Algorithms
(Institute of Advanced Engineering and Science (IAES), 2024-10-15) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
Social Media Addiction Analysis Based on Machine Learning
(Daffodil International University, 23-02-18) Mim, Minjun Nahar; Tazim, Tahrima; Saha, Shama
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.