Social Media And Students Cognition : A Machine Learning Approach Using Random Forest Classifier
DOI:
https://doi.org/10.29284/46xb9e56Keywords:
Random Forest, Boosting, Bagging, Ensemble methods, students, social media.Abstract
Social media networkshave become a major communicator in today's modern world. The main objective of the study is to find out how social networks affect students' behavior. Consideration is alsogiven to whether trust, social media participation, and social media behaviors are positively linked. In order to collect primary data, a general sweep model was used to observe the attitude of undergraduate and post-graduate students aged 18 to 21. 95% of respondents, responded to the questionnaire for which descriptive statistics were appliedto analyse data. Random Forest, Boosting, Voting, and Bagging have been used as a standard set of techniquesin this work. Usingthese ensemble methods researchers got good results that demonstrated the reliability of the proposed model. For greater productivity, the different classifiers are brought together and subsequently added to the overall ensemble method using the Voter procedure. The findings suggest that the impact of social media can be positive or negative, as this research narrowly identifies the real impact of social media sites. The goal of this study is to identify the reasons why students today suffer from mental illnesses such as depression, anxiety, stress, and the cognitive-behavioral changes of the students. Social media websites such as Instagram, Google+, Facebook, and WhatsApp draw students' attention to their studies and have a positive or negative impact on their grade points. The results of the implementation show that the bagging method improved the existing model, where the precision of the bagging algorithm with bagging is 92.6%. Recall outcomes of prediction 1 to prediction2 are improved from 0.90 to 0.97and accuracy results also increased from 0.60 to 0.72.
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