Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Henriques, G.: A Vision for psychological check-ups. Psychology Today (2014). https://www.psychologytoday.com/blog/theory-knowledge/201405/vision-psychological-check-ups
Martin, F.: Perceptions of links between quality of life areas: implications for measurement and practice. Soc. Indic. Res. 106(1), 95–107 (2012)
Buffardi, L.E., Campbell, W.K.: Narcissism and social networking web sites. Pers. Soc. Psychol. Bull. 34(10), 1303–1314 (2008)
Kluemper, D.H., Rosen, P.A.: Future employment selection methods: evaluating social networking web sites. J. Manag. Psychol. 24(6), 567–580 (2009)
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)
Wilson, K.F.: Psychological predictors of young adults’ use of social networking sites. Cyberpsychology, Behav. Soc. Networking 13(2), 173–177 (2012)
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)
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)
Crimson Hexagon (2015). http://www.crimsonhexagon.com/
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)
Clarabridge (2015). http://www.clarabridge.com/
Radian6 (2015). https://radian6.com/
Sysomos (2015). https://sysomos.com/
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)
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)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
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)
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))
Pennacchiotti, M., Popescu, A.M.: A machine learning approach to twitter user classification. ICWSM 11(1), 281–288 (2011)
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 2010
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)
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)
Scholtz, J.: Beyond usability: evaluation aspects of visual analytic environments. In: 2006 IEEE Symposium on Visual Analytics Science and Technology, pp. 145–150 (2006)
Nafari, M., Weaver, C.: Query2Question: translating visualization interaction into natural language. IEEE Trans. Visual. Comput. Graphics 21(6), 756–769 (2015)
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)
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)
Dwyer, T.: Scalable, versatile and simple constrained graph layout. Comput. Graphics Forum 28(3), 991–998 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Tran, J., Nguyen, Q.V., Simoff, S., Huang, M.L. (2016). Areas of Life Visualisation: Growing Data-Reliance. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-46771-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46770-2
Online ISBN: 978-3-319-46771-9
eBook Packages: Computer ScienceComputer Science (R0)