Cyberemotions pp 187-206 | Cite as

An Agent-Based Modeling Framework for Online Collective Emotions

  • David Garcia
  • Antonios Garas
  • Frank Schweitzer
Part of the Understanding Complex Systems book series (UCS)


Online communication takes a variety of shapes in the different technological media that allow users interact with each other, with their friends, or with arbitrarily large groups. These serve as breeding grounds for collective emotions, in which large amounts of users share emotional states through time. We present our modeling framework for collective emotions in online communities, which can be adapted for the different kinds of online interaction present in the cyberspace. This framework allows the design of agent-based models, in which agents’ emotional states are represented according to psychological theories. This approach aims at a unification of modeling efforts, connecting the sentiment analysis of big data with psychological experiments, through tractable agent-based models. We illustrate the applications of this framework to different online communities, including product reviews, chatrooms, virtual realities, and social networking sites. We show how our model reproduces properties of collective emotions in the reviews of Amazon, and the group discussions of IRC channels. We comment the applications of this framework for data-driven simulation of emotions, and how we formulate testable hypotheses of emotion dynamics for future research on the field.


Emotional State Emotional Expression Multiagent System Emotional Content Sentiment Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has received funding from the European Community’s Seventh Framework Programme FP7-ICT-2008-3 under grant agreement no 231323 (CYBEREMOTIONS).


  1. Ahn, J., Gobron, S., Garcia, D., Silvestre, Q., Thalmann, D., Boulic, R.: An NVC emotional model for conversational virtual humans in a 3D chatting environment. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds.) Articulated Motion and Deformable Objects: 7th International Conference, AMDO 2012, Port d’Andratx, Mallorca, July 11–13, 2012, Proceedings. Lecture Notes in Computer Science (Image Processing, Computer Vision, Pattern Recognition, and Graphics), vol. 7378, pp. 47–57 (2012). doi:10.1007/978-3-642-31567-1_5Google Scholar
  2. Alvarez, R., Garcia, D., Moreno, Y., Schweitzer, F.: Sentiment cascades in the 15M movement. EPJ Data Sci. 4 (1), 6 (2015). doi:10.1140/epjds/s13688-015-0042-4CrossRefGoogle Scholar
  3. Bollen, J., Gonçalves, B., Ruan, G., Mao, H.: Happiness is assortative in online social networks. Artif. Life 17 (3), 237–251 (2011). doi:10.1162/artl_a_00034CrossRefGoogle Scholar
  4. Borge-Holthoefer, J., Moreno, Y.: Absence of influential spreaders in rumor dynamics. Phys. Rev. E 85 (2), 026116 (2012). doi:10.1103/PhysRevE.85.026116ADSCrossRefGoogle Scholar
  5. Chmiel, A., Sienkiewicz, J., Thelwall, M., Paltoglou, G., Buckley, K., Kappas, A., Hołyst, J.A.: Collective emotions online and their influence on community life. PLoS ONE 6 (7), e22207 (2011). doi:10.1371/journal.pone.0022207ADSCrossRefGoogle Scholar
  6. Czaplicka, A., Hołyst, J.A.: Modeling of Internet influence on group emotion. Int. J. Mod. Phys. C 23 (3), 1250020 (2012). doi:10.1142/S0129183112500209ADSCrossRefzbMATHGoogle Scholar
  7. Garas, A., Garcia, D., Skowron, M., Schweitzer, F.: Emotional persistence in online chatting communities. Sci. Rep. 2, 402 (2012). doi:10.1038/srep00402ADSCrossRefGoogle Scholar
  8. Garcia, D., Schweitzer, F.: Emotions in Product Reviews – Empirics and Models. In: Proceedings of 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, PASSAT/SocialCom, pp. 483–488 (2011). doi:10.1109/PASSAT/SocialCom.2011.219Google Scholar
  9. Garcia, D., Garas, A., Schweitzer, F.: Positive words carry less information than negative words. EPJ Data Sci. 1 (1), 3 (2012). doi:0.1140/epjds3Google Scholar
  10. Garcia, D., Zanetti, M.S., Schweitzer, F.: The role of emotions in contributors activity: a case study on the GENTOO community. In: 2013 Third International Conference on Cloud and Green Computing (CGC), pp. 410–417 (2013). doi:10.1109/CGC.2013.71Google Scholar
  11. Gianotti, L.R.R., Faber, P.L., Schuler, M., Pascual-Marqui, R.D., Kochi, K., Lehmann, D.: First valence, then arousal: the temporal dynamics of brain electric activity evoked by emotional stimuli. Brain Topogr. 20 (3), 143–156 (2008). doi:10.1007/s10548-007-0041-2CrossRefGoogle Scholar
  12. Golder, S.A., Macy, M.W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333 (6051), 1878–1881 (2011). doi:10.1126/science.1202775ADSCrossRefGoogle Scholar
  13. Kappas, A.: Emotion and regulation are one! Emotion Rev. 3 (1), 17–25 (2011). doi:10.1177/1754073910380971CrossRefGoogle Scholar
  14. Kuppens, P., Oravecz, Z., Tuerlinckx, F.: Feelings change: accounting for individual differences in the temporal dynamics of affect. J. Pers. Soc. Psychol. 99 (6), 1042–60 (2010). doi:10.1037/a0020962CrossRefGoogle Scholar
  15. Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’09, pp. 497–506. ACM, New York, NY (2009). doi:10.1145/1557019.1557077Google Scholar
  16. Lorenz, J.: Universality in movie rating distributions. Eur. Phys. J. B 71 (2), 251–258 (2009). doi:10.1140/epjb/e2009-00283-3ADSMathSciNetCrossRefGoogle Scholar
  17. Mitrović, M., Tadić, B.: Dynamics of bloggers’ communities: bipartite networks from empirical data and agent-based modeling. Physica A 391 (21), 5264–5278 (2012). doi:10.1016/j.physa.2012.06.004ADSCrossRefGoogle Scholar
  18. Onnela, J.P., Reed-Tsochas, F.: Spontaneous emergence of social influence in online systems. Proc. Natl. Acad. Sci. U. S. A. 107 (43), 18375–18380 (2010). doi:10.1073/pnas.0914572107ADSCrossRefGoogle Scholar
  19. Radicchi, F.: Human activity in the web. Phys. Rev. E 80 (2), 026118 (2009). doi:10.1103/PhysRevE.80.026118ADSMathSciNetCrossRefGoogle Scholar
  20. Rank, S.: Docking agent-based simulation of collective emotion to equation-based models and interactive agents. In: McGraw, R., Imsand, E., Chinni, M.J. (eds.) Proceedings of the 2010 Spring Simulation Multiconference on - SpringSim’10, p. 6. Society for Computer Simulation International, San Diego, CA (2010). doi:10.1145/1878537.1878544Google Scholar
  21. Rank, S., Skowron, M., Garcia, D.: Dyads to groups: modeling interactions with affective dialog systems. Int. J. Comput. Linguistic Res. 4 (1), 22–37 (2013)Google Scholar
  22. Rimé, B.: Emotion elicits the social sharing of emotion: theory and empirical review. Emot. Rev. 1 (1), 60–85 (2009). doi:10.1177/1754073908097189MathSciNetCrossRefGoogle Scholar
  23. Rimé, B., Finkenauer, C., Luminet, O., Zech, E., Philippot, P.: Social sharing of emotion: new evidence and new questions. Eur. Rev. Soc. Psychol. 9 (1), 145–189 (1998). doi:10.1080/14792779843000072CrossRefGoogle Scholar
  24. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39 (6), 1161–1178 (1980). doi:10.1037/h0077714CrossRefGoogle Scholar
  25. Schweitzer, F.: Brownian Agents and Active Particles. Collective Dynamics in the Natural and Social Sciences. Springer Series in Synergetics, 1st edn. Springer, Berlin, Heidelberg (2003). doi:10.1007/978-3-540-73845-9Google Scholar
  26. Schweitzer, F., Garcia, D.: An agent-based model of collective emotions in online communities. Eur. Phys. J. B 77 (4), 533–545 (2010). doi:10.1140/epjb/e2010-00292-1ADSCrossRefGoogle Scholar
  27. Schweitzer, F., Hołyst, J.A. (2000). Modelling collective opinion formation by means of active Brownian particles. Eur. Phys. J. B 15 (4), 723–732. doi:10.1007/s100510051177ADSCrossRefGoogle Scholar
  28. Sienkiewicz, J., Skowron, M., Paltoglou, G., Hołyst, J.: Entropy-growth-based model of emotionally charged online dialogues. Adv. Comput. Syst 16 (04n05), 1350026 (2013). doi:10.1142/S0219525913500264Google Scholar
  29. Smith, E.R., Conrey, F.R.: Agent-based modeling: a new approach for theory building in social psychology. Pers. Soc. Psychol. Rev. 11 (1), 87–104 (2007). doi:10.1177/1088868306294789CrossRefGoogle Scholar
  30. Thelwall, M., Buckley, K., Paltoglou, G., Skowron, M., Garcia, D., Gobron, S., Ahn, L., Kappas, A., Küster, D., Hołyst, J.: Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs. Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, vol. 7817, pp. 1–12 (2013). doi:10.1007/978-3-642-37256-8_1CrossRefGoogle Scholar
  31. Walter, F.E., Battiston, S., Schweitzer, F.: Personalised and dynamic trust in social networks. In: Bergman, L., Tuzhilin, A., Burke, R., Felfering, A., Schmidt-Thieme, L. (eds.) Proceedings of the 3rd ACM conference on Recommender systems - RecSys ’09. ACM Press, New York, NY, pp. 197–204 (2009). doi:10.1145/1639714.1639747Google Scholar
  32. Wu, Y., Zhou, C., Xiao, J., Kurths, J., Schellnhuber, H.J.: Evidence for a bimodal distribution in human communication. Proc. Natl. Acad. Sci. U. S. A. 107 (44), 18803–18808 (2010). doi:10.1073/pnas.1013140107ADSCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • David Garcia
    • 1
  • Antonios Garas
    • 1
  • Frank Schweitzer
    • 1
  1. 1.ETH ZürichZürichSwitzerland

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