CFP last date
20 June 2024
Reseach Article

Popularity Analysis on Social Network: A Big Data Analysis

Published on June 2015 by Sufal Das, Brandon Victor Syiem, Hemanta Kumar Kalita
International Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN2014 - Number 1
June 2015
Authors: Sufal Das, Brandon Victor Syiem, Hemanta Kumar Kalita
d299017b-8aab-464f-a431-08ec2b73ac88

Sufal Das, Brandon Victor Syiem, Hemanta Kumar Kalita . Popularity Analysis on Social Network: A Big Data Analysis. International Conference on Computing, Communication and Sensor Network. CCSN2014, 1 (June 2015), 27-31.

@article{
author = { Sufal Das, Brandon Victor Syiem, Hemanta Kumar Kalita },
title = { Popularity Analysis on Social Network: A Big Data Analysis },
journal = { International Conference on Computing, Communication and Sensor Network },
issue_date = { June 2015 },
volume = { CCSN2014 },
number = { 1 },
month = { June },
year = { 2015 },
issn = 0975-8887,
pages = { 27-31 },
numpages = 5,
url = { /proceedings/ccsn2014/number1/21420-5016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing, Communication and Sensor Network
%A Sufal Das
%A Brandon Victor Syiem
%A Hemanta Kumar Kalita
%T Popularity Analysis on Social Network: A Big Data Analysis
%J International Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN2014
%N 1
%P 27-31
%D 2015
%I International Journal of Computer Applications
Abstract

A social network is a social structure made up of a set of social actors. These actors form a network of social interactions and personal relationships. These networks are a valuable source of information about the users. Thus, analyzing these social interactions (particularly from more popular social networks such as Twitter, Facebook, etc. ) allow us to predict the interests of users from a common place, group, friend circle, etc. From a business point of view, it helps by analyzing the popularity of products that are so often advertised in social networks, by looking at how many users have visited the product page, or how many people have liked the product. In similar context, the popularity of a group or person can help conclude the result of certain events such as elections. This paper explores the popularity index of different politicians in Twitter using MapReduce. We focused on tracking mainly politicians. For each person, we have tracked a list of associated words and counted the frequencies that these words appear in tweets as well as number of followers.

References
  1. Brooker, R. G. 2003. Methods of Measuring Public Opinion. Central Washington University USA.
  2. Flaounas, I. , Sudhahar, S, Lansdall Welfare, T. , Hensiger, E. , Cristianini, N. 2002. Big Data Analysis of News and Social Media Content. Intelligent Systems Laboratory, University of Bristol.
  3. Flaounas, I, Ali, O, Turchi, M, Snowsill, T, Nicart, F, De Bie, T, Cristianini, N. 2011. NOAM: news outlets analysis and monitoring system, SIGMOD Conference, ACM, pp. 1275-1278.
  4. Big Data for Development: Challenges and Opportunities. Global Pulse, 2012.
  5. Letouze, E. 2011. Big Data for Development: Opportunities & Challenges.
  6. Boyd, D. and Crawford, K. "Critical Questions for Big Data". Information, Communication and Society, 2012 15(5):662-679.
  7. Valova, I. and Noirhomme, M. 2008. Processing Of Large Data Sets: Evolution, Opportunities and Challenges. Proceedings of PCaPAC08.
  8. Bakshi, K. 2012. Considerations for Big Data: Architecture and Approach. IEEE.
  9. Zikopoulos, P. , Eaton, C. , DeRoos, D. , Deutsch, T. and Lapis, G. 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Companies, Incorporated.
  10. Big Data: The next frontier for innovation, competition, and productivity. 2011. McKinsey & Company.
  11. Advanced 'Big Data' Analytics with R and Hadoop. 2011. Revolution Analytics White Paper.
  12. McKendrick. J. 2012. Big Data, Big Challenges, Big Opportunities. 2012 IOUG Big Data Strategies Survey.
  13. Nandimath, J. , Patil, A. , Banerjee, E. , Kakade, P. 2013. Big Data Analysis Using Apache Hadoop. IEEE IRI, pp-700-703 USA.
  14. Lewis, D. , Yang, Y. , Rose, T. G. and Li F. "RCV1: A New Benchmark Collection for Text Categorization Research", Journal of Machine Learning Research 5, 2004. pp. 361–397.
  15. Lee, K. H. , Lee, Y. J. , Choi, H. , Chung, Y. D. and Moon, B. 2012. Parallel data processing with MapReduce: a survey. ACM SIGMOD Record. Vol. 40, no. 4, pp. 11-20.
  16. Parker, C. 2012. Unexpected challenges in large scale machine learning. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine '12, pages 1-6, New York, USA 2012 ACM.
  17. Gopalkrishnan, V. , Steier, D. , Lewis, H. and Guszcza, J. 2012. Big data, big business: bridging the gap. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, programming Models and Applications, Big-Mine '12, pages 7-11, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Analysis Big Data Techniques Popularity Analysis And Mapreduce.