Areas of Life Visualisation: Growing Data-Reliance

  • Jesse TranEmail author
  • Quang Vinh Nguyen
  • Simeon Simoff
  • Mao Lin Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9929)


This paper presents a framework to mine and identify the areas of life and the way they are perceived, understood cognitively, and effectively using visualisation and machine learning. We provide an overview of the network of users including their activity and connections as well as zoom and details on demand of each individual areas of life. This research identifies the factors of each area of life which are significant on the user’s social media profile in relation to information associated with each user such as time and location, including dynamic social behaviours. It aims to identify the key psychological factors and salient behaviours in order to find out the psychological factors of the user, and other overheads that can be portrayed in an image.


Data visualisation Areas of life Social networks Psychology Dynamic social behaviours Twitter Machine learning 


  1. 1.
    Henriques, G.: A Vision for psychological check-ups. Psychology Today (2014).
  2. 2.
    Martin, F.: Perceptions of links between quality of life areas: implications for measurement and practice. Soc. Indic. Res. 106(1), 95–107 (2012)CrossRefGoogle Scholar
  3. 3.
    Buffardi, L.E., Campbell, W.K.: Narcissism and social networking web sites. Pers. Soc. Psychol. Bull. 34(10), 1303–1314 (2008)CrossRefGoogle Scholar
  4. 4.
    Kluemper, D.H., Rosen, P.A.: Future employment selection methods: evaluating social networking web sites. J. Manag. Psychol. 24(6), 567–580 (2009)CrossRefGoogle Scholar
  5. 5.
    Livingstone, S.: Taking risky opportunities in youthful content creation: teenagers’ use of social networking sites for intimacy, privacy and self-expression. New Media Soc. 10(3), 393–411 (2008)CrossRefGoogle Scholar
  6. 6.
    Wilson, K.F.: Psychological predictors of young adults’ use of social networking sites. Cyberpsychology, Behav. Soc. Networking 13(2), 173–177 (2012)CrossRefGoogle Scholar
  7. 7.
    Yu, A.Y., Tian, S.W., Vogel, D., Kwok, R.C.W.: Can learning be virtually boosted? an investigation of online social networking impacts. Comput. Educ. 55(4), 1494–1503 (2010)CrossRefGoogle Scholar
  8. 8.
    Jin, L.,, Wen, Z.: An augmented social interactive learning approach through Web 2.0. In: 33rd Annual IEEE International Computer Software and Applications Conference (COMPSAC 2009), pp. 607–611 (2009)Google Scholar
  9. 9.
    Crimson Hexagon (2015).
  10. 10.
    Tran, J., Nguyen, Q.V., Simoff, S.: IntelliViz- a tool for visualizing social networks with hashtags. In: Bebis, G., et al. (eds.) ISVC 2014, Part II. LNCS, vol. 8888, pp. 894–903. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Clarabridge (2015).
  12. 12.
    Radian6 (2015).
  13. 13.
    Sysomos (2015).
  14. 14.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation, pp. 1320–1326 (2010)Google Scholar
  15. 15.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: The Workshop on Languages in Social Media, pp. 30–38 (2011)Google Scholar
  16. 16.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  17. 17.
    Marafino, B.J., Davies, J.M., Bardach, N.S., et al.: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. J. Am. Med. Inform. Assoc. 21(5), 871–875 (2014)CrossRefGoogle Scholar
  18. 18.
    Häkkinen, J., Tian, J.: N-gram and decision tree based language identification for written words. In: 2001 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU 2001), pp. 335–338 ((2001))Google Scholar
  19. 19.
    Pennacchiotti, M., Popescu, A.M.: A machine learning approach to twitter user classification. ICWSM 11(1), 281–288 (2011)Google Scholar
  20. 20.
    Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44. ACM, October 2010Google Scholar
  21. 21.
    Krämer, N.C., Winter, S.: Impression management 2.0: The relationship of self-esteem, extraversion, self-efficacy, and self-presentation within social networking sites. J. Media Psychol. 20(3), 106–116 (2008)CrossRefGoogle Scholar
  22. 22.
    Donalek, C., Djorgovski, S. G., Cioc, A., et al.: Immersive and collaborative data visualization using virtual reality platforms. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 609–614 (2014)Google Scholar
  23. 23.
    Scholtz, J.: Beyond usability: evaluation aspects of visual analytic environments. In: 2006 IEEE Symposium on Visual Analytics Science and Technology, pp. 145–150 (2006)Google Scholar
  24. 24.
    Nafari, M., Weaver, C.: Query2Question: translating visualization interaction into natural language. IEEE Trans. Visual. Comput. Graphics 21(6), 756–769 (2015)CrossRefGoogle Scholar
  25. 25.
    Mizuno, H., Mori, Y., Taniguchi, Y., Tsuji, H.: Data queries using data visualization techniques. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, pp. 2392–2396 (1997)Google Scholar
  26. 26.
    Wong, P.C., Shen, H.W., Johnson, C.R., Chen, C., Ross, R.B.: The top 10 challenges in extreme-scale visual analytics. IEEE Comput. Graphics Appl. 32(4), 63 (2012)CrossRefGoogle Scholar
  27. 27.
    Dwyer, T.: Scalable, versatile and simple constrained graph layout. Comput. Graphics Forum 28(3), 991–998 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jesse Tran
    • 1
    Email author
  • Quang Vinh Nguyen
    • 1
  • Simeon Simoff
    • 1
  • Mao Lin Huang
    • 2
  1. 1.MARCS Institute and School of Computing, Engineering and MathematicsWestern Sydney UniversityPenrithAustralia
  2. 2.School of Software, Faculty of Engineering and ITUniversity of TechnologySydneyAustralia

Personalised recommendations