An experiential investigation on internet addiction and specific disorders like depression, self-esteem and introversion among university students

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Abstract

The tormenting hurdle that is slowly isolating the youth of this generation from being leaders of tomorrow, is depression. In recent times, students have been observed to indulge in compulsive use of Internet which has a relation to the massive upsurge of depressed individuals among the youth. Hence, the main and foremost objective of this research is to find correlation among the leading disorders which are Internet addiction, depression, self-esteem and introversion which can diminish these predicaments. In order to serve this purpose, 461 undergraduate students have been selected arbitrarily from several institutions of Bangladesh and were solicited to complete a standard questionnaire which was prepared based on the self-reported measures concerning the disorders mentioned before. The correlational survey design which was employed through social media contains Internet Addiction Test by Dr. Kimberly Young, Rosenberg Self-Esteem Scale by M. Rosenberg, PROMIS Emotional Distress-Depression short scale and Introversion scale by McCroskey. In pursuit of extracting desired results from the survey as well as to check accuracy, numerous methods have been utilized. Cronbach alpha provided the proof that the data retrieved from the survey is consistent. Subsequently, Chi-square test and ANOVA procured that positive correlations exists among internet addiction, depression, self-esteem and introversion. To corroborate this newly established correlation, multifarious machine learning techniques have been adopted and experimented. These experiments revealed that Internet addiction can predict self-esteem, depression can predict self-esteem and depression can predict introversion. After applying these methods, we came to the conclusion that there exists correlation among the psychological syndromes mentioned above and that this correlation can be exploited and manoeuvred to a positive outcome whose aim will be to reduce the severity of these disorders.

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Includes bibliographical references (pages 34-36).
Cataloged from PDF version of thesis.
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

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Thesis