Framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model

  • Muruganantham A.Email author
  • G. Meera Gandhi


The Social, Mobile, Analytics and Cloud (SMAC) explosion in recent times changed the way the customers look at and collaborate businesses through large world of data or information, often described as “Big Data”. With over 1590 million active users, social media such as Facebook, Twitter, WhatsApp, Instagram, LinkedIn etc. send or receive messages or post or access new content every day. Businesses or enterprises understand and extract useful insights from social media platform and transforming it into useful information or knowledge along with their enterprise business data for strategic decision making. A framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model is proposed for social media data and our comprehensive study on large-scale twitter dataset experiment explains how MCDM (TOPSIS) method outperforms against the standard centrality methods using predicted Spreading Size. Two well-known metrics such as MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) are applied to measure prediction accuracy of our MCDM based method against standard methods. TOPSIS (MCDM based) experiences least error accuracy of 9% in MAE and 16% in RMSE than the standard methods to prove that the proposed approach works better than the standard methods.


Social media analytics Centrality measures Spreading size  MAE RMSE Twitter data set 



  1. 1.
    Amiri M (2010) Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Syst Appl 37(9):6218–6224CrossRefGoogle Scholar
  2. 2.
    Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an Influencer: Quantifying Influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, (WSDM ‘11), pp. 65–74Google Scholar
  3. 3.
    Behzadian M, Kazemzadeh R, Albadvi A, Aghdasi M (2010) PROMETHEE: A comprehensive literaturereview on methodologies and applications. Eur J Oper Res 200(1):198–215CrossRefGoogle Scholar
  4. 4.
    Behzadian M, Otaghsara S, Yazdani M, Ignatius J (2012) A state-of-the-art survey of TOPSIS applications. Expert Syst Appl 39(17):13051–13069CrossRefGoogle Scholar
  5. 5.
    Bentes A, Carneiro J, Silva J, Kimura H (2012) Multidimensional assessment of organizational performance: Integrating BSC and AHP. J Bus Res 65(12):1790–1799CrossRefGoogle Scholar
  6. 6.
    P Brodka, “Key User Extraction Based on Telecommuniatoin Data (aka. Key Users in Social Network. How to find them?) 2013, arXiv:1302.1369Google Scholar
  7. 7.
    Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring User Influence in Twitter: The Million Follower Fallacy. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 10–17Google Scholar
  8. 8.
    Chen SJ, Hwang CL (1992) Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, BerlinCrossRefGoogle Scholar
  9. 9.
    Di Gangi PM, Wasko M (2009) Steal My Idea! Organizational Adoption of User Innovations From a User Innovation Community: A Case Study of Dell IdeaStorm. Decis Support Syst 48(1):303–3012CrossRefGoogle Scholar
  10. 10.
    Domingos P, Richardson M (2001) Mining the network value of customers. ACM SIGKDD:57–66Google Scholar
  11. 11.
    Gandhi M, Muruganantham A (2015) Potential Influencers Identification using Multi-criteria Decision Making (MCDM) methods. Procedia Computer Science 57:1179–1188CrossRefGoogle Scholar
  12. 12.
    Ghosh R, Lerman K (2010) Predicting Influential Users in Online Social Networks. In: SNA-KDD Proceedings of KSS workshop on Social Network Analysis. arXiv:1005.4882Google Scholar
  13. 13.
    Gobeck J, Hendler J (2006) Inferring binary trust relationships in web-based social networks ACM. TIOT:497–529Google Scholar
  14. 14.
    Grul D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. ACM, In WWW:491–501Google Scholar
  15. 15.
    Han H, Trimi S (2018) A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Syst Appl 103:133–145CrossRefGoogle Scholar
  16. 16.
    Hwang CL, Yoon K (1981) Multiple Attribute Decision Making Methods and Applications. Springer, Berlin HeidelbergCrossRefGoogle Scholar
  17. 17.
    Kaplan AM, Haenlein M (2010) Users of the World, Unite! The Challenges and Opportunities of Social Media. Business Horizons 53(1):61CrossRefGoogle Scholar
  18. 18.
    Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network”, In SIGKDD, pp. 137–146Google Scholar
  19. 19.
    Kiron D, Ferguson RB, Prentice PK (2013) From Value to Vision: Reimagining the Possible with Data Analytics. MIT Sloan Management Review: Spring Research Report, pp. 1–19Google Scholar
  20. 20.
    Kohavi R, Rothleder N, Simoudis E (2002) Emerging Trends in Business Analytics. Commun ACM 45(8):45–48CrossRefGoogle Scholar
  21. 21.
    Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sust Energ Rev 69:596–609CrossRefGoogle Scholar
  22. 22.
    Li L, Peng W, Kataria S, Sun T, Li T (2015) Recommending Users and communities in Social media. ACM Transactions on Knowledge Discovery from Data (TKDD) 10(17)CrossRefGoogle Scholar
  23. 23.
    Li J, Peng W, Li T, Sun T, Li Q, Xu J (2014) Social network user influence sense-making and dynamics prediction. Expert Syst Appl 41(11):5115–5124CrossRefGoogle Scholar
  24. 24.
    Li Q, Zhou T, Lu L, Chen D (2014) Identifying influential spreaders by weighted LeaderRank. PhysicaA: Statistical Mechanics and its Applications 404:47–55MathSciNetCrossRefGoogle Scholar
  25. 25.
    Muruganantham A, Gandhi M (2016) Discovering and Ranking Influential Users in Social Media Networks Using Multi-Criteria Decision Making (MCDM) Methods. Indian J Sci Technol 9(32):1–11CrossRefGoogle Scholar
  26. 26.
    Musial K, Kazienko P, Brodka P (2009) User Position Measures in Social Networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis. ACM, New York, No. 6.Google Scholar
  27. 27.
    Petz G, Karpowica M, Furchu H, Auinger A, Stritestky V, Holzinger A (2015) Computational approaches for mining user’s opinions on the Web 2.0. Inf Process Manag:510–519Google Scholar
  28. 28.
    Richardson M, Domingos P (2002) Mining knowledge sharing sites for viral marking. SIGKDD:61–70Google Scholar
  29. 29.
    Riquelme F, Gonazalez P (2016) Measuring user influence on Twitter: A survey. Information Processing and Management 52(5):949–975CrossRefGoogle Scholar
  30. 30.
    Shin H, Xu Z, Kim EY (2008) Discovering and Browsing of Power Users by Social Relationship Analysis in Large-Scale Online Communities. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 1:105–111CrossRefGoogle Scholar
  31. 31.
    X. Song, Y. Chi, K. Hino, B. Tseng (2007) Information flow modeling based on diffusion rate for prediction and ranking. In WWW, pp. 191–200Google Scholar
  32. 32.
    Song X, Tseng BL, Lin CY, Sun MT (2006) Personalized recommendation driven by information flow. SIGKDD:509–516Google Scholar
  33. 33.
    Spertus E, Sahami M, Buyukkokten O (2005) Evaluating Similarity Measures: A large-scale study in the Orkut social network. ACM SIGKDD:678–684Google Scholar
  34. 34.
    TOPSIS. [Cited 2015 Oct 01]. Available from:
  35. 35.
    Tran KT (2013) Introduction to Web Services with Java. Bookboon, LondonGoogle Scholar
  36. 36.
    Ul Mustafa R, Nawaz MS, Ullah Lali MI, Zia T, Mehmood W (2017) Predicting the cricket match outcome using crowd opinions on social Networks: A comparative study of machine learning methods. Malaysian Journal of Computer Science 30(1):63–76CrossRefGoogle Scholar
  37. 37.
    Uzunkaya C, Ensari T, Kavurucu Y (2015) Hadoop Ecosystem and Its Analysis on Tweets. Procedia Soc Behav Sci 195:1890–1897CrossRefGoogle Scholar
  38. 38.
    Vasuki V, Natarajan N, Lu Z, Savas B, Dhillon L (2011) Scalable Affiliation Recommendation using Auxiliary Networks. ACM Trans Intell Sys TechnologyGoogle Scholar
  39. 39.
    Velasquez M, Patrick T (2013) Hester, An Analysis of Multi-Criteria Decision Making Methods. International Journal of Operations Research 10(2):56–66MathSciNetGoogle Scholar
  40. 40.
    Vijayakumar A (2017) Automated risk identification using NLP in cloud based development environments. J Ambient Intell Humaniz Comput PP:1–13Google Scholar
  41. 41.
    Wang X, Triantaphyllou E (2008) Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega 33(1):45–63CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science & EngineeringSathyabama Institute of Science and TechnologyChennaiIndia

Personalised recommendations