28Jun 2017

CHARACTERISING USER DEMOGRAPHICS ACROSS SOCIAL NETWORK.

  • Research Scholar,YMCAUST, Faridabad.
  • Assistant professor, CE, YMCAUST, Faridabad.
  • Abstract
  • Keywords
  • References
  • Cite This Article as
  • Corresponding Author

Analysis of the social media platform like Facebook & Twitter reveals a huge amount of information through user generated profile and comments. With the growing popularity of social media (Twitter), social network remains the largest as well as the most popular network. With registration of new users, tweets, news article, a user uploads their personal information or article and give his/her views about the some article. It contains huge amount of information about the user demographics, views about the video or article. This paper proposes a classification method for defining user behaviour by mining the user generated texts.


  1. Mukhopadhyay, D., & Kulkarni, S. (2017). An Approach to Design an IoT Service for Business?Domain Specific Web Search. In Proceedings of the International Conference on Data Engineering and Communication Technology (pp. 621-628). Springer Singapore.
  2. Sun, G., Xie, Y., Liao, D., Yu, H., & Chang, V. (2017). User-defined privacy location-sharing system in mobile online social networks. Journal of Network and Computer Applications, 86, 34-45. [3] F. Wu and B. A.
  3. Tang, L., Chen, H., Ku, W. S., & Sun, M. T. (2017). Exploiting location-aware social networks for efficient spatial query processing. GeoInformatica, 21(1), 33-55.
  4. Malhotra, A., Totti, L., Meira Jr, W., Kumaraguru, P., & Almeida, V. (2012, August). Studying user footprints in different online social networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on (pp. 1065-1070). IEEE.
  5. Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129-152.
  6. Chakrabarti. Mining the Web: Discovering Knowledge from Hypertext Data. Morgan-Kauffman, 2002.
  7. M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 423?430, Sydney, Australia, July 2006. Association for Computational Linguistics.
  8. Gull, Angadi, dr.santoshkumar gandhi : tracing high quality content in social media for modelling & predicting the flow of information ? a case study on facebook. In: international journal of emerging trends & technology in computer science volume 2 issue 2, April 2013
  9. Zhou, M., Zhang, W., Smith, B., Varga, E., Farias, M., & Badenes, H. (2012, February). Finding someone in my social directory whom i do not fully remember or barely know. In Proceedings of the 2012 ACM international conference on Intelligent User Interfaces (pp. 203-206). ACM.
  10. Noor, S., & Martinez, K. (2009, June). Using social data as context for making recommendations: an ontology based approach. In Proceedings of the 1st Workshop on Context, Information and Ontologies (p. 7). ACM.
  11. Mendes, P. N., Jakob, M., & Bizer, C. (2012, May). DBpedia: A Multilingual Cross-domain Knowledge Base. In LREC (pp. 1813-1817).
  12. Dalvi, B., Minkov, E., Talukdar, P. P., & Cohen, W. W. (2015, February). Automatic gloss finding for a knowledge base using ontological constraints. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (pp. 369-378). AC

[Charu Virmani and Anuradha Pillai. (2017); CHARACTERISING USER DEMOGRAPHICS ACROSS SOCIAL NETWORK. Int. J. of Adv. Res. 5 (Jun). 2308-2312] (ISSN 2320-5407). www.journalijar.com


Charu Virmani
MRIU

DOI:


Article DOI: 10.21474/IJAR01/4673      
DOI URL: https://dx.doi.org/10.21474/IJAR01/4673