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An Agent Architecture for Simulating Communication Dynamics in Social Media

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Multiagent System Technologies (MATES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10413))

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Abstract

Social media like Facebook, Twitter, or Google+ have become important communication channels. Nonetheless, the distribution and dynamics of that communication make it difficult to analyze and understand. To overcome this, we propose an agent architecture for modeling and simulating user behavior to analyze communication dynamics in social media. Our agent decision-making method utilizes sociological actor types to represent motivations of media users and their impact on communicative behavior. We apply this concept to a simulation of real world Twitter communication accompanying a German television program. Our evaluation shows that the agent architecture is capable of simulating communication dynamics in human media usage.

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References

  1. Balke, T., Gilbert, N.: How do agents make decisions? a survey. J. Artif. Soc. Soc. Simul. 17(4), 13 (2014)

    Google Scholar 

  2. Belkaroui, R., Faiz, R., Elkhlifi, A.: Conversation analysis on social networking sites. In: 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 172–178. IEEE (2014)

    Google Scholar 

  3. Berger, C.R.: Interpersonal communication. The International Encyclopedia of Communication (2008)

    Google Scholar 

  4. Berndt, J.O., Herzog, O.: Anticipatory behavior of software agents in self-organizing negotiations. In: Nadin, M. (ed.) Anticipation Across Disciplines. CSM, vol. 29, pp. 231–253. Springer, Cham (2016). doi:10.1007/978-3-319-22599-9_15

    Chapter  Google Scholar 

  5. Berndt, J.O., Rodermund, S.C., Lorig, F., Timm, I.J.: Modeling user behavior in social media with complex agents. In: Third International Conference on Human and Social Analytics (HUSO 2017). IARIA (2017, to appear)

    Google Scholar 

  6. Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. In: 5th International AAAI Conference on Weblogs and Social Media, pp. 450–453 (2011)

    Google Scholar 

  7. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99(3), 7280–7287 (2002)

    Article  Google Scholar 

  8. Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10. IEEE (2010)

    Google Scholar 

  9. Bratman, M.E.: Intention, Plans, and Practical Reason. Harvard University Press, Cambridge (1987)

    Google Scholar 

  10. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)

    Google Scholar 

  11. Davidsson, P.: Agent based social simulation: a computer science view. J. Artif. Soc. Soc. Simul. 5(1) (2002)

    Google Scholar 

  12. Dittrich, P., Kron, T.: Complex reflexive agents as models of social actors. In: Proceedings of the SICE Workshop on Artificial Society/Organization/Economy, Meeting of Systems Engineering, Tokyo, vol. 25, pp. 79–88, (2002)

    Google Scholar 

  13. Fischer, K., Florian, M., Malsch, T.: Socionics: Scalability of Complex Social Systems. Springer, Berlin (2005)

    Book  Google Scholar 

  14. Flam, H.: Emotional ‘man’: I. the emotionalman’and the problem of collective action. Int. Sociol. 5(1), 39–56 (1990)

    Article  Google Scholar 

  15. Hedström, P., Ylikoski, P.: Causal mechanisms in the social sciences. Annu. Rev. Sociol. 36, 49–67 (2010)

    Article  Google Scholar 

  16. Hoste, V., Van Hee, C., Poels, K.: Towards a framework for the automatic detection of crisis emotions on social media: a corpus analysis of the tweets posted after the crash of germanwings flight 9525. In: 2nd International Conference on Human and Social Analytics (HUSO 2016), pp. 29–32 (2016)

    Google Scholar 

  17. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65. ACM (2007)

    Google Scholar 

  18. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  19. Kirby, J.: Connected Marketing: The Viral, Buzz and Word of Mouth Revolution. Butterworth-Heinemann, Amsterdam (2010)

    Google Scholar 

  20. Lerman, K., Ghosh, R.: Information contagion: an empirical study of the spread of news on digg and twitter social networks. ICWSM 10, 90–97 (2010)

    Google Scholar 

  21. Lorig, F., Timm, I.J.: How to model the human factor for agent-based simulation in social media analysis? In: 2014 ADS Symposium (Part of SpringSim Multiconference), p. 12. SCS (2014)

    Google Scholar 

  22. Luhmann, N.: Social Systems. Stanford University Press, Stanford (1995)

    Google Scholar 

  23. Maireder, A., Schlögl, S.: 24 hours of an# outcry: the networked publics of a socio-political debate. Eur. J. Commun. 29(6), 1–16 (2014)

    Article  Google Scholar 

  24. Monica, S., Bergenti, F.: An analytic study of opinion dynamics in multi-agent systems with additive random noise. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS, vol. 10037, pp. 105–117. Springer, Cham (2016). doi:10.1007/978-3-319-49130-1_9

    Chapter  Google Scholar 

  25. Monica, S., Bergenti, F.: Opinion dynamics in multi-agent systems: selected analytic models and verifying simulations. Comput. Math. Organ. Theory 23(87), 1–28 (2016)

    Google Scholar 

  26. Neuendorf, K.A.: The Content Analysis Guidebook. Sage, Thousand Oaks (2016)

    Google Scholar 

  27. Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44. ACM (2010)

    Google Scholar 

  28. Rao, S., Georgeff, M.P.: BDI agents: from theory to practice. In: Lesser, V.R., Gasser, L. (eds.) Proceedings of the First International Conference on MultiAgent Systems (ICMAS 1995), pp. 312–319. The MIT Press, Boston (1995)

    Google Scholar 

  29. Schirra, S., Sun, H., Bentley, F.: Together alone: motivations for live-tweeting a television series. In: 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 2441–2450. ACM (2014)

    Google Scholar 

  30. Schweitzer, F., Hołyst, J.A.: Modelling collective opinion formation by means of active brownian particles. Eur. Phys. J. B-Condens. Matter Complex Syst. 15(4), 723–732 (2000)

    Article  Google Scholar 

  31. Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)

    Article  MathSciNet  Google Scholar 

  32. Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic. PLoS ONE 6(5), e19467 (2011)

    Article  Google Scholar 

  33. Timm, I., Berndt, J., Lorig, F., Barth, C., Bucher, H.J.: Dynamic analysis of communication processes using twitter data. In: 2nd International Conference on Human and Social Analytics (HUSO 2016), pp. 14–22. IARIA (2016)

    Google Scholar 

  34. Vandenhoven, S., De Clercq, O.: What does the bird say? exploring the link between personality and language use in dutch tweets. In: 2nd International Conference on Human and Social Analytics (HUSO 2016), pp. 38–42. IARIA (2016)

    Google Scholar 

  35. Vega-Redondo, F.: Complex Social Networks. Cambridge University Press, Cambridge (2007)

    Book  MATH  Google Scholar 

  36. Zhang, C., Sun, J., Wang, K.: Information propagation in microblog networks. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 190–196. ACM (2013)

    Google Scholar 

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Acknowledgments

We thank Carla Schmidt, Christof Barth, and Hans-Jürgen Bucher for providing us with the data set and a media studies perspective on our application example.

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Correspondence to Jan Ole Berndt .

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Rodermund, S.C., Lorig, F., Berndt, J.O., Timm, I.J. (2017). An Agent Architecture for Simulating Communication Dynamics in Social Media. In: Berndt, J., Petta, P., Unland, R. (eds) Multiagent System Technologies. MATES 2017. Lecture Notes in Computer Science(), vol 10413. Springer, Cham. https://doi.org/10.1007/978-3-319-64798-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-64798-2_2

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