Advertisement

Clustering and Social Recommendation Applied in Health Community of Practice

  • Meriem HafidiEmail author
  • El Hassan Abdelwahed
  • Sara Qassimi
  • Rachid Lamrani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)

Abstract

Social networks are increasingly used to exchange information. The social users are the main origin of the shared web resources and contents. However, they are also influenced by these shared data. The exchanges and interactions produced are an important element for defining the profiles of these users. In this paper, we investigate modeling of individuals using a user-centered model, in particular the activity and social pressure features. We propose a user profile enrichment approach based on extracted tags from shared resources. Our goal is to link similar users in order to build sub-networks according to users’ profiles. Thus, determining the central and important nodes in the network will establish basis for the web resources recommendation, information diffusion and community resuscitation. Our research will interest doctors’ communities to share their knowledge through network. It will teach the most basic health care information to the patients of certain chronic diseases such as diabetes.

Keywords

Social networks User profile Dynamic of interactions and communities detection Recommendation Collective intelligence Health community of practice 

References

  1. 1.
    Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. University of California, Berkeley (2005)Google Scholar
  2. 2.
    Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, HICSS 2010. IEEE Computer Society (2010)Google Scholar
  3. 3.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: Proceedings of the 2010 IEEE Second International Conference on Social Computing, SOCIALCOM 2010. IEEE Computer Society (2010)Google Scholar
  4. 4.
    Lagnier, C.: Information Diffusion within the social networks. Diffusion de l’information dans les réseaux sociaux, Intelligence Artificielle. Université de Grenoble (2013)Google Scholar
  5. 5.
    Wenger, E., McDermott, R., Snyder, W.: Cultivating Communities of Practice: A Guide to Managing Knowledge. Harvard Business School Press, Boston (2002). Amy HI Lee received the MBA degree from the University of British Columbia, CanadaGoogle Scholar
  6. 6.
    Wenger, E., McDermott, R., Snyder, W.: A Guide to Managing Knowledge: Cultivating Communities of Practice. Harvard Business School Press, Boston (2002)Google Scholar
  7. 7.
    Ranmuthugala, G., Plumb, J.J., Cunningham, F.C., Georgiou, A., Westbrook, J.I., Braithwaite, J.: How and why are communities of practice established in the healthcare sector? A systematic review of the literature. BMC Health Serv. Res. 11, 273 (2011)CrossRefGoogle Scholar
  8. 8.
    Mačiulienė, M., Skaržauskienė, A.: Emergence of collective intelligence in online communities. J. Bus. Res. 69, 1718–1724 (2016). Mykolas Romeris University, LithuaniaCrossRefGoogle Scholar
  9. 9.
    Scarlat, E., Maries, I.: Simulating collective intelligence of communities of practice using agent based methods. In: Agent and Multi-Agent Systems: Technologies and Applications. LNCS, vol. 6070, pp. 305–314 (2010)CrossRefGoogle Scholar
  10. 10.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  11. 11.
    Hu, J., Wang, B., Tao, Z.: Personalized tag recommendation using social contacts. In: Proceedings of the Workshop SRS 2011, in conjunction with CSCW (2011)Google Scholar
  12. 12.
    Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: Discovering shared conceptualizations in folksonomies. Web Semant. 6, 38–53 (2008)CrossRefGoogle Scholar
  13. 13.
    Mezghani, M., et al.: From the influence of user profile enrichment on buzz propagation in social media. Experiments on delicious. J. Sci. Technol. Inf. Série ISI Ingénierie Systèmes d’Information, Lavoisier 21(4), 67–81 (2016)Google Scholar
  14. 14.
    Ben Hiba, L.: Evaluating virtual teams in social and collaborative platforms based on SNA and BI analaytics. ENSIAS. Mohammed 5 University (2014)Google Scholar
  15. 15.
    Vallet, J.: Where Social Networks, Graph Rewriting and Visualisation Meet: Application to Network Generation and Information Diffusion. Other [cs.OH]. Bordeaux University (2017)Google Scholar
  16. 16.
    Cha, M., Antonio, J., Prez, N., Haddadi, H.: Flash floods and ripples: the spread of media content through the blogosphere. In: Proceedings of the 3rd AAAI International Conference on Weblogs and Social Media, ICWSM 2009 (2009)Google Scholar
  17. 17.
    Furnham, A., Crump, J.: The Myers-Briggs type indicator (MBTI) and promotion at work. Psychology 6, 1510–1515 (2015).  https://doi.org/10.4236/psych.2015.612147CrossRefGoogle Scholar
  18. 18.
    Hasan, O., Habegger, B., Brunie, L., Bennani, N., Damiani, E.: A discussion of privacy challenges in user profiling with big data techniques: the EEXCESS use case. In: 2013 IEEE International Congress on Big Data, pp. 25–30. IEEE (2013)Google Scholar
  19. 19.
    Meo, P.D., Ferrara, E., Abel, F., Aroyo, L., Houben, G.-J.: Analyzing user behavior across social sharing environments. ACM Trans. Intell. Syst. Technol. (TIST) 5(1), 14 (2014)Google Scholar
  20. 20.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of twitter posts for user profile construction on the social web. In: Extended Semantic Web Conference, pp. 375–389. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Benammar, A., Hubert, G., Mothe, J.: Automatic profile reformulation using a local document analysis. In: European Conference on Information Retrieval, pp. 124–134. Springer, Heidelberg (2002)Google Scholar
  22. 22.
    Qassimi, S., Abdelwahed, E.H., Hafidi, M., Lamrani, R.: Towards an emergent semantic of web resources using collaborative tagging. In: Model and Data Engineering, MEDI 2017. LNCS, vol. 10563. Springer, Cham (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meriem Hafidi
    • 1
    Email author
  • El Hassan Abdelwahed
    • 1
  • Sara Qassimi
    • 1
  • Rachid Lamrani
    • 1
  1. 1.Faculty of Sciences Semlalia Marrakech FSSMCadi Ayyad UniversityMarrakeshMorocco

Personalised recommendations