DISCOVERING CRITICAL SITUATIONS IN ONLINE SOCIAL NETWORKS - A Neuro Fuzzy Approach to Alert Marketing Managers

Carolin Kaiser, Sabine Schlick, Freimut Bodendorf

Abstract

More and more people are exchanging their opinions in online social networks and influencing each other. It is crucial for companies to observe opinion formation concerning their products. Thus, risks can be recognized early on and counteractive measures can be initiated by marketing managers. A neuro fuzzy approach is presented which allows the detection of critical situations in the process of opinion formation and the alerting of marketing managers. Rules for identifying critical situations are learned on the basis of the opinions of the network members, the influence of the opinion leaders and the structure of the network. The opinions and characteristics of the network are identified by text mining and social network analysis. The approach is illustrated by an exemplary application.

References

  1. Bodendorf, F., Kaiser, C., (2009). Detecting Opinion Leaders and Trends in Online Social Networks. In Proceedings of the 2nd Workshop on Social Web Search and Mining. Hong Kong.
  2. Borgelt, C., Klawonn, F., Kruse, R., Nauck, D., 2003. Neuro-Fuzzy-Systeme: Von den Grundlagen k√ľnstlicher Neuronaler Netze zur Kopplung mit Fuzzy Systemen. [engl.: Neuro-Fuzzy-Systems: Foundations of the combination of neural networks and fuzzysystems.] (3th ed.). Wiesbaden: Vieweg.
  3. Chang, C. L., Chen, D. Y., and Chuang, T. R., (2002). Browsing Newsgroups with a Social Network Analyzer. In Proceedings of the Sixth International Conference on Information Visualization, London.
  4. Choudhury, M. D., Sundaram, H., John, A., Seligmann, D. D. (2007): Contextual Prediction of Communication Flow in Social Networks. In Proceedings of the IEEE/WIC/ACM international Conference on Web intelligence (Silicon Valley, California, USA). Web Intelligence. IEEE Computer Society, Washington, DC, pp. 57-65.
  5. Choudhury, M. D. (2009). Modelling and Predicting Group Activity over Time in Online Social Media. In Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia. Torino, Italy.
  6. Choudhury, M. D., Sundaram, H.,John, A., Seligmann, D. D. (2009). Which are the Representatative Groups in a Community? Extracting and Characterizing Key Groups in Blogs. ACM Student Research Competition, HyperText 7809.
  7. Cortes C., Vapnik V. N., 1995. Support Vector Networks. In Machine Learning, Vol. 20, pp. 273-297.
  8. Dave, K., Lawrence, S., Pennock, D., M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web.
  9. Dhar, V., Chang, E. (2007). Does Chatter Matter? The Impact of User-Generated Content on Music Sales. Technical Report, Leonard N. Stern School of Business, New York University.
  10. Glance, N., Hurst, M., Nigam, K., Siegler, M., Stockton, R., Tomokiyo, T. (2005). Deriving Marketing Intelligence from Online Discussion. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. Chicago, Illinois, USA, pp. 419 - 428.
  11. Gomez, V., Kaltenbrunner, A., and Lopez, V. (2008). Statistical Analysis of the Social Network and Discussion Threads in Slashdot, In Proceedings of the International World Wide Web Conference, Beijing: ACM Press.
  12. Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A. (2005). The Predictive Power of Online Chatter. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. Chicago, Illinois, USA, pp. 78 - 87.
  13. Huang, Y., Liu, S., Wang, Y. (2007). Online Detecting and Tracking of the Evolution of User Communities. In Third International Conference on Natural Computation, pp.681-685.
  14. Kaiser, C. (2009): Combining Text Mining and Data Mining For Gaining Valuable Knowledge from Online Reviews. In Pedro Isaías (Ed.). IADIS International Journal on WWW/Internet 6 (2), pp. 63-78, 2009.
  15. Kaiser, C., Bodendorf, F. (2009). Opinion and Relationship Mining in Online Forums. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. Milan, pp. 128-131.
  16. Katz E., Lazarsfeld P. F, (1955). Personal influence, the part played by people in the flow of mass communication, Glencoe: Free Press.
  17. Keller E. B., Berry J., (2003). The influentials, New York: Free Press.
  18. Kim, S.-M., Hovy, E. (2007). Crystal: Analysing Predictive Opinions on the Web. In proceedings of the 2007 Joint Conference on the Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, pp. 1056-1064.
  19. Nauck, D., Kruse, R., (1994). A Fuzzy Perceptron as a Generic Model for Neuro-Fuzzy Approaches. In Fuzzy Systeme 7894.
  20. Nauck, D., Kruse, R., (1995). NEFCLASS - A NeuroFuzzy Approach for the Classification of Data. In George, K.M., Carrol, J. H., Deaton, E., Oppenheim, D. Hightower, J. (Ed.), (1995). Applied Computing 1995: Proc. of the 1995 ACM Symposium on Applied Computing, Nashville, Feb. pp. 26-28. ACM Press.
  21. Nauck, D., Klawonn, F., Kruse, R., (1997). Foundations of neuro-fuzzy systems. Chichester: John-Wiley & Sons.
  22. Nauck, D., Kruse, R., (1997). A neuro-fuzzy method to learn fuzzy classification rules from data. In Fuzzy Sets and Systems 1997 (89), pp. 277-288.
  23. Nauck, U., (1999). Design and Implementation of a Neuro-Fuzzy Data Analysis Tool in Java. Diploma Thesis, University of Braunschweig, Braunschweig.
  24. Onishi, H., Manchanda, P. (2009): Marketing Activity, Blogging and Sales. Technical Report, Ross School of Business, University of Michigan.
  25. Pang P., Lee L., and Vaithyanathan S., (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the conference on empirical methods in natural language processing. ACM, pp. 79-86.
  26. Popescu, A.-M., Etzioni, O. (2005). Extracting Product Features and Opinions from Reviews. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 339-346.
  27. Rogers E., (2003). Diffusion of innovations (5th ed.). New York: Free Press.
  28. Scott J., (2000). Social Network Analysis - A Handbook. London: SAGE.
  29. Tong, R. M., Yager, R. R. (2004). Characterizing Attitudinal Behaviors in On-Line Open-Source. Proceedings of Association for the Advancement of Artificial Intelligence. Spring Symposium 2004, Atlanta.
  30. Valente T. W., (1999). Network Models of the Diffusion of Innovations, Cresskill: Hampton Press.
  31. Viermetz, M., Skubacz, M., Ziegler, C.-N.; Seipel, D. (2005). Tracking Topic Evolution in News Environments. In 10th IEE Conference on E-commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services, pp. 215- 220.
  32. Wang, L.-X., Mendel, J.M., (1991). Generating Rules by Learning from Examples. In Int. Symposium on Intelligent Control. Piscataway, NJ, USA: IEEE Press, pp.263-268.
  33. Wang, L.-X., Mendel, J.M. (1992). Generating Fuzzy Rules by Learning from Examples. IEEE Trans. Systems, Man, and Cybernetics, 22 (6), pp. 1414-1427. Piscataway, NJ, USA: IEEE Press.
  34. Wassermann, S., Faust, K., (1999). Social Network Analysis - Methods and Applications. Cambridge: Cambridge University Press.
  35. Weiss S., Indurkhya N., Zhang T., Damerau F. (2005). Text Mining - Predictive Methods for Analyzing unstructured Information. New York: Springer.
  36. Welser, H. T., Gleave, E., Fisher, D., Smith, M. (2007). Visualizing the Signatures of Social Roles in Online Discussion Groups. In Journal of Social Structure, Vol. 8.
  37. Zadeh, L., (1965). Fuzzy Sets. In Information and Control 8 1965 (3), San Diego, CA, USA: Academic Press, pp. 338-353.
Download


Paper Citation


in Harvard Style

Kaiser C., Schlick S. and Bodendorf F. (2010). DISCOVERING CRITICAL SITUATIONS IN ONLINE SOCIAL NETWORKS - A Neuro Fuzzy Approach to Alert Marketing Managers . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 56-64. DOI: 10.5220/0003070900560064


in Bibtex Style

@conference{kdir10,
author={Carolin Kaiser and Sabine Schlick and Freimut Bodendorf},
title={DISCOVERING CRITICAL SITUATIONS IN ONLINE SOCIAL NETWORKS - A Neuro Fuzzy Approach to Alert Marketing Managers},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={56-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003070900560064},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - DISCOVERING CRITICAL SITUATIONS IN ONLINE SOCIAL NETWORKS - A Neuro Fuzzy Approach to Alert Marketing Managers
SN - 978-989-8425-28-7
AU - Kaiser C.
AU - Schlick S.
AU - Bodendorf F.
PY - 2010
SP - 56
EP - 64
DO - 10.5220/0003070900560064