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Analysis on Social Media Addiction using Data Mining Technique

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
D. Radha, R. Jayaparvathy, D. Yamini

D Radha, R Jayaparvathy and D Yamini. Article: Analysis on Social Media Addiction using Data Mining Technique. International Journal of Computer Applications 139(7):23-26, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {D. Radha and R. Jayaparvathy and D. Yamini},
	title = {Article: Analysis on Social Media Addiction using Data Mining Technique},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {7},
	pages = {23-26},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The popularity of social media applications has changed the way of communication for the past few years. The transfer of information from one individual to another had grown beyond the basic act of texting and evolved to enable the transfer of other media such as image, audio, video. An example of such an application is WhatsApp. WhatsApp is a popular application which is used widely for texting, calling, transferring media. This paper attempts to classify the behavioral aspects of a user to predict if they are addicted or not. The positive impacts of using a smart phone application on online businesses and how WhatsApp has become a trendsetter among the youngsters of the current generation and the increasing frequency of its users among college students have been discussed by prior works. The objective of this paper is to predict whether a particular individual is said to be addicted to WhatsApp or not. The conclusion is expected to expose the level of addiction to WhatsApp. It is expected to be high assuming the increasing density of users. Data mining is the practice of examining large pre-existing databases in order to generate new information.


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WhatsApp, Psychology, User behavior, Technological improvements, Communication, Response level, Usage of app, Data mining