Skip to main content
Log in

Simulating the spread of opinions in online social networks when targeting opinion leaders

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

An increasing number of people are joining online social networks. By interacting with each other, network members influence one another’s opinion. These influencing effects can be utilized by marketing. A wave of influence can be triggered by addressing only a few opinion leaders in the network. Targeting the right opinion leaders is a big challenge. This paper presents a new approach which simulates the spread of opinions when influencing certain opinion leaders. In contrast to other approaches, the influencing effects are not assumed but revealed by real data. The principles of opinion formation are detected by ant mining algorithms before they are applied to simulate the spread of opinions. The approach is applied to an online gaming community and provides valuable insights for marketing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. WEKA can be downloaded from the website: http://www.cs.waikato.ac.nz/ml/weka/.

  2. The Java Universal Network/Graph Framework can be downloaded from http://jung.sourceforge.net/.

  3. The GUI Ant-Miner Tool can be downloaded from http://sourceforge.net/projects/guiantminer/.

References

  • Agrawal R et al (2003) Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th international conference on World Wide Web. New York, pp 529–535

  • Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD international conferences on knowledge discovery and data mining, Philadelphia

  • Bass F (1969) A new product growth model for consumer durables. Manage Sci 15:215–227

    Article  Google Scholar 

  • Blum C, Li X (2008) Swarm intelligence in optimization. In: Blum C, Merkle D (eds) Swarm intelligence—introduction and applications. Springer, Berlin, pp 43–86

    Chapter  Google Scholar 

  • 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

  • Bromley DB (2000) Psychological aspects of corporate identity, image and reputation. Corp Reput Rev 3(3):240–252

    Article  Google Scholar 

  • Brown J, Reinegen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362

    Article  Google Scholar 

  • Brüne G (1989) Meinungsführerschaft im Konsumgütermarketing [opinion leadership in consumer marketing]. Physica-Verlag, Heidelberg

    Book  Google Scholar 

  • Burmaster A, Lee L, McGiboney M (2009) Personal recommendations and consumer opinions posted online are the most trusted forms of advertising globally. http://blog.nielsen.com/nielsenwire/wp-content/uploads/2009/07-/pr_global-study_07709.pdf, Download 06 Dec 2010

  • Carnes T, Nagarajan C, Wild SM, van Zuylen A (2007) Maximizing influence in a competitive social network: a follower’s perspective. In: Proceedings of the 9th international conference on electronic commerce, Minneapolis

  • Cha M, Mislove A, Grummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th international conference on World Wide Web

  • Chang C-L, Chen D-Y, Chuang T-R (2002) Browsing newsgroups with a social network analyzer. In: Proceedings of the international conference on information visualization. IEEE Computer Society, Los Alamitos

  • Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD conference on knowledge discovery and data mining, Paris

  • Cialdini RB, Goldstein NJ (2004) Social influence: compliance and conformity. Annu Rev Psychol 55:591–621

    Article  Google Scholar 

  • Coombs WT (2007) Protecting Organization reputations during a crisis: the development and application of situational crisis communication theory. Corp Reput Rev 10:163–176

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Crutchfield RS (1955) Conformity and character. Am Psychol 10:191–198

    Article  Google Scholar 

  • Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web. ACM Press, Budapest, pp 519–528

  • Deutsch M, Gerhard HB (1955) A study of normative and informative social influences upon individual judgment. J Abnorm Soc Psychol 51:629–636

    Article  Google Scholar 

  • Domingos P, Pazzani MJ (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2–3):103–130

    Article  Google Scholar 

  • Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 57–66

  • Fan T-K, Chang C-H (2010) Sentiment-oriented contextual advertising. Knowl Inf Syst 23(3):321–344

    Article  Google Scholar 

  • Feldman S (1966) Motivational aspects of attitudinal elements and their place in cognitive interaction. In: Feldman S (ed) Cognitive consistency. Academic Press, New York

    Google Scholar 

  • Felser G (2007) „Werbe- und Konsumentenpsychologie“ [psychology of advertisment and consumption], 3rd edn. Spektrum Akademischer Verlag, Berlin

    Google Scholar 

  • Feng S, Wang D, Yu G, Gao W, Wong K-F (2011) Extracting common emotions from blogs based on fine-grained sentiment clustering. Knowl Inf Syst 27(2):281–302

    Article  Google Scholar 

  • Fredkin NE, Johnson EC (1999) Social influence networks and opinion change. Adv Group Process 16:1–29

    Google Scholar 

  • Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27(1):51–90

    Google Scholar 

  • Gladwell M (2000) The tipping point. Little Brown and Company, London

    Google Scholar 

  • Glance M et al (2005) Deriving marketing intelligence from online discussion. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery in data mining. Chicago, pp 419–428

  • Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223

    Article  Google Scholar 

  • Goldstone RL, Janssen M (2005) Computational models of collective behavior. Trends Cogn Sci 9:424–429

    Article  Google Scholar 

  • Goldstone RL, Jones A, Roberts ME (2006) Group path formation. IEEE Trans Syst Man Cybern 36:611–620

    Article  Google Scholar 

  • Granovetter MS (1978) Threshold models of collective behavior. Am J Sociol 83:1420–1443

    Article  Google Scholar 

  • Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through Blogspace. In: Proceedings of the 13th international conference on World Wide Web, New York

  • Hartline J, Mirrokni VS, Sundararajan M (2008) Optimal marketing strategies over social networks. In: Proceedings of the 17th international conference on World Wide Web, Beijing

  • Hauser JR, Urban GL, Weinberg BD (1993) How consumers allocate their time when searching for information. J Mark Res 30:452–466

    Article  Google Scholar 

  • Herr PM, Kardes FR (1991) Kim J Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. J Consum Res 17:454–462

    Article  Google Scholar 

  • Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276

    Article  Google Scholar 

  • Kaiser C, Bodendorf F (2012) Mining consumer dialog in online forums. Int Res 22(3):275–297

    Google Scholar 

  • Kaiser C, Kröckel J, Bodendorf F (2010) Ant-based simulation of opinion spreading in online social networks. In: Proceedings of the 2010 IEEE/WIC/ACM international joint conference on web intelligence and intelligent agent technologies, Toronto

  • Kaiser C, Kröckel J, Bodendorf F (2011) Analyzing opinion formation in online social networks—mining services for online market research. In: Proceedings of the SRII global conference, San Jose, pp 384–391

  • Kalynam K, Mcintyre S, Masonis JT (2007) Adaptive experimentation in interactive marketing, the case of viral marketing at Plaxo. J Interact Mark 21(3):72–85

    Google Scholar 

  • Kanouse DE, Hanson LR (1972) Negativity in evaluations. In: Jones EE et al (eds) Attribution: perceiving the causes of behavior. General Learning Press, Morristown

    Google Scholar 

  • Kao A, Poteet S (2007) Overview. In: Kao A, Poteet SR (eds) Natural language processing and text mining. Springer, London, pp 1–7

    Chapter  Google Scholar 

  • Katz E, Lazarsfeld PF (1955) Personal influence, the part played by people in the flow of mass communication. Free Press, Glencoe

    Google Scholar 

  • Keller EB, Berry J (2003) The influentials. Free Press, New York

    Google Scholar 

  • Kelley HH (1952) The two functions of reference groups. In: Swanson GE, Newcomb TM, Hartley EL (eds) Readings in social psychology. Holt, New York, pp 410–414

    Google Scholar 

  • Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence in a social network. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining

  • Kohavi R (1995) The power of decision tables. In: Proceedings of the 8th European conference on machine learning, pp 174–189

  • Kroeber-Riel W, Weinberg P (2009) „Konsumentenverhalten“ [consumer behavior], 9th edn. Verlag Vahlen, München

    Google Scholar 

  • Leskovec J, Adamic LA, Huberman BA (2006) The dynamics of viral marketing. In: Proceedings of the ACM conference on electronic commerce, Ann Arbor

  • Li H, Bhowmick SS, Sun A (2009) Blog cascade affinity: analysis and prediction. In: Proceedings of the 18th ACM conferences information and knowledge management, Hong Kong

  • Li YM, Lai CY, Chen CW (2009) Identifying bloggers with marketing influence in the blogosphere. In: Proceedings of the 11th international conference on electronic commerce, Taipei

  • Liu B, Hu M, Cheng J (2005) opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web. ACM, New York, pp 342–351

  • Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference information and knowledge management, Napa Valley

  • McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: an integrative typology. Inform Syst Res 13(3):334–359

    Article  Google Scholar 

  • Pang P, Lee L, 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

  • Park C, Lee TM (2009) Information direction, website reputation and eWOM effect: a moderating role of product type. J Bus Res 62(1):61–67

    Article  Google Scholar 

  • Parpinelli R, Lopes H, Freitas A (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6(4):321–332

    Article  Google Scholar 

  • Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. In: Kao A, Poteet SR (eds) Natural language processing and text mining. Springer, London, pp 9–28

    Chapter  Google Scholar 

  • Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo

  • Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: ACM SIGKDD international conference on knowledge discovery and data mining

  • Rogers EM (1995) Diffusion of innovations, 4th edn. Free Press, New York

    Google Scholar 

  • Saito K, Kimura M, Ohara K, Motoda H (2011) Efficient discovery of influential nodes for SIS models in social networks. Knowl Inform Syst. doi:10.1007/s10115-011-0396-2

  • Schelling TC (1971) Dynamic models of segregation. J Math Sociol 1:143–186

    Article  Google Scholar 

  • Scott JP (2000) Social network analysis: a handbook, 2nd edn. Sage Publications, Thousand Oaks

    Google Scholar 

  • Sen S, Lerman D (2007) Why are you telling me this? An examination into negative consumer reviews on the web. J Interact Mark 21(4):76–94

    Article  Google Scholar 

  • Technical Assistance Research Programs (1981) Measuring the grapevine—consumer response and word-of-mouth. Washington, DC

    Google Scholar 

  • Tucker L, Melewar T (2005) Corporate reputation and crisis management: the threat and manageability of anti-corporatism. Corp Reput Rev 7(4):377–387

    Article  Google Scholar 

  • Valente TW (1999) Network models of the diffusion of innovations. Hampton Press, Cresskill

    Google Scholar 

  • Wassermann S, Faust K (1999) Social network analysis—methods and applications. Cambridge University Press, Cambridge

    Google Scholar 

  • Weiss SM et al (2005) Text mining—predictive methods for analyzing unstructured information. Springer, NY

    Google Scholar 

  • Yang Y, Liu X (1999) A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, Berkeley, pp 42–49

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carolin Kaiser.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kaiser, C., Kröckel, J. & Bodendorf, F. Simulating the spread of opinions in online social networks when targeting opinion leaders. Inf Syst E-Bus Manage 11, 597–621 (2013). https://doi.org/10.1007/s10257-012-0210-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-012-0210-z

Keywords

Navigation