CFP last date
20 June 2024
Reseach Article

Analysis on Social Media Addiction using Data Mining Technique

by D. Radha, R. Jayaparvathy, D. Yamini
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 7
Year of Publication: 2016
Authors: D. Radha, R. Jayaparvathy, D. Yamini
10.5120/ijca2016909200

D. Radha, R. Jayaparvathy, D. Yamini . Analysis on Social Media Addiction using Data Mining Technique. International Journal of Computer Applications. 139, 7 ( April 2016), 23-26. DOI=10.5120/ijca2016909200

@article{ 10.5120/ijca2016909200,
author = { D. Radha, R. Jayaparvathy, D. Yamini },
title = { Analysis on Social Media Addiction using Data Mining Technique },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 7 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number7/24504-2016909200/ },
doi = { 10.5120/ijca2016909200 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:42.006731+05:30
%A D. Radha
%A R. Jayaparvathy
%A D. Yamini
%T Analysis on Social Media Addiction using Data Mining Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 7
%P 23-26
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Jamaluddin Ibrahim, Rafidah Chee Ros, Nurul Faatihah Sulaiman, Roszaini Che Nordin,& Li Ze, “Positive impact of smartphone applications: WhatsApp & Facebook for Online business”, IJSRP
  2. Khyati M Patel ,”Customer satisfaction towards WhatsApp” , Golden Jubilee Institute of Business Administration
  3. Dr. P. Uma Maheswari , “Frequency of using WhatsApp Messenger among college students in Salem district, TamilNadu”, IJCSMA
  4. Ms. Jisha K , Dr. Jebakumar ,”WhatsApp: A Trend Setter in Mobile Communication among Chennai Youth”, IOSR
  5. Richard Shambare Tshwane , ”The Adoption of WhatsApp: Breaking the Vicious Cycle of Technological Poverty in South Africa”, Journal of Economics and Behavioral Studies
  6. Dan Bouhnik , Mor Deshen, “WhatsApp goes to school:Mobile Instant Messaging between teachers and students”, JITE
  7. Johnson Yeboah,Geoge Dominic, “The Impact of WhatsApp messenger usage on students performance in tertiary institutions in Ghana”,Journal of Education and Practice
  8. Neelamadhab Padhay,Dr. Pragnyaban Mishra,Rasmita Panigrahi, “The survey of data mining applications and features”, IJCSEIT
  9. Nitin Agarwal, Ehtesham Haque,Huan Liu,Lance Parsons, “Research paper recommender systems: A subspace clustering approach”, IAC
  10. Arpit Gupta,Ankit Gupta,Amit Mishra, “Research paper on cluster techniques of data variations”, IJATER
Index Terms

Computer Science
Information Sciences

Keywords

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