Classification of Message Spreading in a Heterogeneous Social Network

  • Siwar Jendoubi
  • Arnaud Martin
  • Ludovic Liétard
  • Boutheina Ben Yaghlane
Part of the Communications in Computer and Information Science book series (CCIS, volume 443)

Abstract

Nowadays, social networks such as Twitter, Facebook and LinkedIn become increasingly popular. In fact, they introduced new habits, new ways of communication and they collect every day several information that have different sources. Most existing research works focus on the analysis of homogeneous social networks, i.e. we have a single type of node and link in the network. However, in the real world, social networks offer several types of nodes and links. Hence, with a view to preserve as much information as possible, it is important to consider social networks as heterogeneous and uncertain. The goal of our paper is to classify the social message based on its spreading in the network and the theory of belief functions. The proposed classifier interprets the spread of messages on the network, crossed paths and types of links. We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier.

Keywords

Information propagation heterogeneous social network classification evidence theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, R.M., May, R.M.: Infectious Diseases of Humans. Oxford University Press (1991)Google Scholar
  2. 2.
    Aregui, A., Denœux, T.: Consonant belief function induced by a confidence set of pignistic probabilities. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 344–355. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Aregui, A., Denoeux, T.: Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. Int. J. Approx. Reasoning 49(3), 575–594 (2008)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Dempster, A.P.: Upper and Lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38, 325–339 (1967)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., Kellerer, W.: Outtweeting the twitterers - predicting information cascades in microblogs. In: WOSN 2010, pp. 3–11 (2010)Google Scholar
  6. 6.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12(3), 211–223 (2001)CrossRefGoogle Scholar
  7. 7.
    Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology, 1420–1443 (1978)Google Scholar
  8. 8.
    Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. SIGMOD Rec. 42(1), 17–28 (2013)Google Scholar
  9. 9.
    Hansen, D.L., Shneiderman, B., Smith, M.A.: Analysing social media network with nodeXL insights from a connected world. Elsevier Inc. (2011)Google Scholar
  10. 10.
    He, W., Zhab, S., Li, L.: Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management 33, 464–472 (2013)CrossRefGoogle Scholar
  11. 11.
    Jousselme, A.L., Grenier, D., Bossé, E.: A new distance between two bodies of evidence. Information Fusion 2, 91–101 (2001)CrossRefGoogle Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM Press (2003)Google Scholar
  13. 13.
    Li, C.T., Lin, S.D., Shan, M.K.: Influence propagation and maximization for heterogeneous social networks. In: WWW 2012-Poster Presentation, pp. 559–560 (April 2012)Google Scholar
  14. 14.
    Lo, Y.W., Potdar, V.: A review of opinion mining and sentiment classification framework in social networks. In: 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST 2009 (June 2009)Google Scholar
  15. 15.
    Mostafa, M.M.: More than words: Social networks text mining for consumer brand sentiments. Expert Systems with Applications 40, 4241–4251 (2013)CrossRefGoogle Scholar
  16. 16.
    Nettleton, D.F.: Survey data mining of social networks represented as graphs. Computer Sciences Review 7, 1–34 (2013)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Newman, M.E.J.: Networks: An introduction. Oxford University Press (2010)Google Scholar
  18. 18.
    Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., Motoda, H.: Learning diffusion probability based on node attributes in social networks. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 153–162. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Sermpezis, P., Spyropoulos, T.: Information diffusion in heterogeneous networks: The configuration model approach. In: 2013 Proceedings IEEE INFOCOM, pp. 3261–3266 (April 2013)Google Scholar
  20. 20.
    Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)Google Scholar
  21. 21.
    Smets, P.: Belief Functions: the Disjunctive Rule of Combination and the Generalized Bayesian Theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Siwar Jendoubi
    • 1
    • 2
  • Arnaud Martin
    • 2
  • Ludovic Liétard
    • 2
  • Boutheina Ben Yaghlane
    • 1
  1. 1.LARODECUniversity of TunisLe BardoTunisie
  2. 2.IRISAUniversity of Rennes 1LannionFrance

Personalised recommendations