Advertisement

Intelligent social network based data modeling for improving health care

  • K. Veningston
  • Seifedine KadryEmail author
  • Haydar Sabeeh Kalash
  • B. Balamurugan
  • R. Sathiyaraj
Original Paper
  • 12 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

The objective of this paper is to measure the influence of user to other user’s opinion in online medical social forum (http://www.medhelp.org/) or twitter about a disease/health condition/surgery/medications and so on. And to help online healthcare community by identifying positive/negative influences. Positive influence on medical condition/treatment/drugs may be favorable to aggrieved people while adverse influence may cause undesirable impacts to other peoples of the same community. Therefore, this paper aims to assess people’s opinion and identify influential users in online healthcare forum using conversation content and network-based properties such as reply relationship and response immediacy which acts as an explicit and implicit measure of collaboration between users. The result of the proposed scheme is evaluated based on two online benchmark medical databases PubMed and WebMD. The experimental results show that the accuracy of predicting influential users is reasonably good in terms of Similarity Index measured between contents written by Influential users and contents available in medical database.

Keywords

Social network Health care Influence user Ranking algorithms Twitter 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Liben-Nowell D, Kleinberg J. Thelink prediction problem for social networks. In: Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM), pp. 556–559, 2003.Google Scholar
  2. 2.
    Wang W, Street WN, A novel algorithm for community detection and influence ranking in social networks, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 555–560, 2014.Google Scholar
  3. 3.
    Shahriary SR, Shahriari M, Noor RMD, A Community-Based Approach for Link Prediction in Signed Social Networks, Scientific Programming, Hindawi Publication. 2015; 2015:602690.Google Scholar
  4. 4.
    Akay A, Dragomir A, Erlandsson B-E. Network-based modeling and intelligent data mining of social media for improving care. IEEE Journal of Biomedical and health informatics. 2015;19(1):210–8.CrossRefGoogle Scholar
  5. 5.
    Xuning Tang CC. Yang. Ranking user influence in healthcare social media. ACM Trans Intell Syst Technol. 2012;3(4):73.Google Scholar
  6. 6.
    Krulwich B, Burkey C. ContactFinder: Extracting indications of expertise and answering questions with referrals. In: Symposium on Intelligent Knowledge Navigation and Retrieval, pp. 85–91, 1995.Google Scholar
  7. 7.
    Anagnostopoulos A, Kumar R, Mahdian M. Influence and correlation in social networks. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 7–15, 2008.Google Scholar
  8. 8.
    Campbell CS, Maglio PP, Cozzi A, Dom B. Expertise identification using email communications. In: Proceedings of the 12th ACM international conference on Information and knowledge management, pp. 528–531, 2003.Google Scholar
  9. 9.
    Zhang J, Ackerman M, Adamic L. Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th ACM international conference on World Wide Web, pp. 221–230, 2007.Google Scholar
  10. 10.
    Tang J, Sun J, Wang C, Yang Z. Social influence analysis in large-scale networks. In: 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 807–816, 2009.Google Scholar
  11. 11.
    Brin S, Page L. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proceedings of the seventh international conference on World Wide Web, Elsevier Journal on Computer Networks and ISDN Systems, vol. 30, no. 1–7, pp. 107–117, 1998.Google Scholar
  12. 12.
    Eirinaki M, Vazirgiannis M. UPR: Usage-based page ranking for web personalization. Proceedings of the fifth IEEE international conference on Data Mining (ICDM), pp. 130–137, 2005.Google Scholar
  13. 13.
    Haveliwala TH. Topic-sensitive PageRank: A context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng. 2003;15(4):784–96.CrossRefGoogle Scholar
  14. 14.
    Trusov M, Bodapati AV, Bucklin RE. Determining Influential Users in Internet Social Networks. Journal of Marketing Research, American Marketing Association, pp. 643–659, 2010.Google Scholar
  15. 15.
    Leenders RTAJ. Modeling Social Influence through Network Autocorrelation: Constructing the Weight Matrix. Soc Networks, Elsevier. 2002;24(1):21–48.CrossRefGoogle Scholar
  16. 16.
    Robins G, Pattison P, Elliott P. Network Models for Social Influence Processes. Psychometrika. 2001;66(2):161–90.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Goyal A, Bonchi F, Lakshmanan LVS. Learning Influence Probabilities In Social Networks, Proceedings of the third ACM international conference on Web search and data mining (WSDM) , pp. 241–250, 2010.Google Scholar
  18. 18.
    Li J, Peng W, Li T, Sun T. Social Network User Influence Dynamics Prediction, Web Technologies and Applications, Volume 7808 of the series Lecture Notes in Computer Science, pp 310–322, 2013.Google Scholar
  19. 19.
    Cosley D, Huttenlocher D, Kleinberg J, Lan X, Suri S. Sequential Influence Models in Social Networks, In Proceedings of Fourth International AAAI Conference on Weblogs and Social Media, 2010.Google Scholar
  20. 20.
    Ghanem AG, Vedanarayanan S, Minai AA, Agents of influence in social networks, In Proceedings of the 11th International Conference on Autonomous Agents and Multi-agent Systems - Volume 1, pp. 551–558, 2012.Google Scholar
  21. 21.
    Ziegler C-N, Lausen G. Propagation models for trust and distrust in social networks. Inf Syst Front. 2005;7(4–5):337–58.CrossRefGoogle Scholar
  22. 22.
    Veningston K, Shanmugalakshmi R, Nirmala V. Semantic Association Ranking Schemes for Information Retrieval Applications using Term Association Graph Representation. Sadhana - Academy Proceedings in Engineering Science, Special issue on Machine Learning for Big Data, Sadhana Vol. 40, Part 6, 2015, pp. 1793–1819Google Scholar
  23. 23.
    Lavanya T, Miraclin Joyce Pamila JC, Veningston K. Online Review Analytics using Word Alignment Model on Twitter Data. In Proc. IEEE International Conference on Advanced Computing and Communication Systems (ICACCS 2016), 2016.Google Scholar
  24. 24.
    Veningston K, Shanmugalakshmi R. Combining User Interested Topic and Document Topic for Personalized Information Retrieval. In: Srinivasa S, Mehta S, editors. Big Data Analytics (BDA), Lecture Notes in Computer Science (LNCS), vol. 8883. Cham: Springer International Publishing Switzerland; 2014. p. 60–79.Google Scholar
  25. 25.
    Mei Q, Cai D, Zhang D, Zhai C. Topic modeling with network regularization. In: Proceedings of the 17th international conference on World Wide Web, pp. 101–110, ACM, 2008.Google Scholar
  26. 26.
    Tang J, Sun J, Wang C, Yang Z. Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 807–816, ACM, 2009.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringMadanapalle Institute of Technology & ScienceMadanapalleIndia
  2. 2.Department of Mathematics and Computer Science, Faculty of ScienceBeirut Arab UniversityBeirutLebanon
  3. 3.Faculty of Computing SciencesGulf College MabelaMuscatSultanate of Oman
  4. 4.School of SCSEGalgotias UniversityGreater NoidaIndia

Personalised recommendations