ABSTRACT
Mining social media is the process of defining, analyzing, and extracting applicative patterns and trends from row social media data. Social media are very popular way of expressing opinions and interacting with many individual in the online world. However growing number of social network delusion among various age categories are recently noted. Mental sickness can have a deep influence on person, families, and society as well. Hence, we propose a framework that analyzes Social Network Delusion (SND) and investigates the addiction ratio. This work first defines the framework for analyzing the social network delusion based on mining online social behavior that provides an early stage opportunity to identify SNDs (Social Network Delusion). The proposed system mainly works in three phases. Feature extraction and analysis of the various posts posted by the users on Facebook, Instagram and Twitter is performed by using mining algorithm in the first step. The SND prediction using the extracted features is done in the second phase; Third phase uses the predicted results as an input for investigating the addiction ratio. We investigate the addiction ratio among different genders and age groups for analyzing the prevention strategies against growing number of SND.
- Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, Hiroyuki Ohsaki. 2015. Recognizing Depression from Twitter Activity. 33rd Annual ACM Conference, CHI 2015, Crossings, Seoul, Korea. doi>10.1145/2702123.2702280.Google ScholarDigital Library
- Abby McCourt. 2018. Social Media Mining: The effects of big data in the age of Social Media, Media Freedom and Information Access Center, Yale Law School.Google Scholar
- Chih-Chung Chang and Chih-Jen-Lin. 2011. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, Vol. 2, Issue 3, Article 27. April 2011. doi>10.1145/1961189.1961199CHI '00. ACM, New York, NY, 526--531. DOI= http://doi.acm.org/10.1145/332040.332491.Google ScholarDigital Library
- Tavel, Kun-Lin Liu, Wu-Jun Li, Minyi Guo. Emoticon Smoothed Language Models for Twitter Sentiment Analysis. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Pages 1678--1684, Toronto, Ontario, Canada - July 22--26, 2012Google Scholar
- Ronan Collobert, Fabian Sinz, JasonWeston. 2006. Large Scale Transductive SVMs. Journal of Machine Learning Research, 7 (2006), pp 1687--1712Google ScholarDigital Library
- Young Min Baek, Young Bae and Hyunmi Jang. 2013. Social and Parasocial Relationships on Social Network Sites and Their Differential Relationships with Users' Psychological Well-being. Cyberpsychology, behavior, and social networking, Volume 16, Number 7, 2013. https://doi.org/10.1089/cyber.2012.0510Google Scholar
- Munmun De Choudhury. 2013. Predicting Depression via Social Media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Copyright 2013.Google Scholar
- K. Gayathri and A. Marimuthu. 2012. Text Document Pre-Processing with the KNN for Classification Using the SVM. Proceedings of7'h International Conference on Intelligent Systems and Control (ISCO 2013) 978-1-4673-4603-0/12Google Scholar
- Tamara G. Kolda, Brett W. Bader. 2009. Tensor Decompositions and Applications. Society for Industrial and Applied Mathematics, Vol. 51, No. 3, pp.455_500Google Scholar
- Ju¨rgen Abel and William Teahan. 2005. Universal Text Preprocessing for Data Compression, IEEE Transactions On Computers, Vol. 54, No. 5, May 2005Google Scholar
- Christopher Steven Marcum. 20. Age Differences in Daily Social Activities. RAND Center for the Study of Aging, (P30AG012815). December 2011, WR-904.Google ScholarCross Ref
- Amanda Lenhart, Kristen Purcell, Aron Smith and Kathryn Zickuhr. 2010. Social Media and Mobile Internet Use Among Teens and young Adults. A project of the Pew Research Centre, February 2010.Google Scholar
- Igor Pantic. 2014. Online Social Networking and Mental Health. Cyberpsychology, Behavior, and Social Networking, Volume 17, Number 10, 2014Google Scholar
- Woochun Jun. 2015. An Analysis Study on Correlation of Internet Addiction and Gender. Proceeding of 2nd International Conference on Information Science and Security (ICISS). DOI: 10.1109/ICISSEC.2015.7370976Google ScholarDigital Library
- Hong-Han Shuai, Chih-Ya Shen. 2016. Mining Online Social Data for Detecting Social Network Mental Disorders. Proceedings of the 25th International Conference on World Wide Web, Pages 275--285. doi>10.1145/2872427.2882996Google ScholarDigital Library
- Yumei Zheng, Dawei Wei, Junlong Li, Tao Zhu. 2016. Internet use and its impact on individual physical health. IEEE Access, Vol 4, pp, 5135--5142. DOI: 10.1109/ACCESS.2016.2602301Google ScholarCross Ref
- K. Chak and L. Leung. 2004. Shyness and locus of control as predictors of internet addiction and internet use. Cyberpsychology, Behavior, and Social Networking, Vol 7(5), pp 559--70. DOI: 10.1089/1094931042403073Google ScholarCross Ref
- Andrade L, Caraveo. 2003. A Epidemiology of major depressive episodes: Results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys.2003. International Journal of Methods Psychiatry, Res.12(1), pp 3--21.Google Scholar
- Alessandro Acquist, Laura Brandimarte, George Loewenstein. 2015. Privacy and human behavior in the age of behavior. Vol. 347, Issue 6221, pp.509--514Google Scholar
- Software and systems engineering Software testing Part 1: Concepts and definitions, 2013. IEEE standards, 2013, pp-1--64.Google Scholar
Index Terms
- Identifying Social Network Delusion to Investigate Addiction Ratio using Data Mining
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