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CECM: A cognitive emotional contagion model in social networks

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Abstract

Social networks have changed the way how people communicate with each other dramatically. The study on information dissemination in social networks has attracted considerable attention. Many evidences show that emotionsof netizens are likely to spread with information and the emotional contagion also affect information dissemination in social networks. In this paper, we propose a cognitive emotional contagion model (CECM) which combines the model of individual characteristics, the topology of a social network, and the changing process as well as evolution of emotion contagion. Distinguished from conventional epidemiology-based models, our CECM model not only considers the changes of individuals’ emotional statuses but also the influence of individuals to others during the information dissemination in a social network. Specifically, CECM first models an individual’s emotions as emotional attributes and emotional statuses. Using Emotional statuses, we define the process of an individual’s emotional change, which could be affected by one’s emotional attributes (e.g. personality). CECM also models the network-wise features of an individual, including one’s authority and the connection strength to one’s neighbors in a social network. Finally, given emotional models and network-wise features of all users in a social network, a series of transition probabilities among the user’s emotional status are defined to model the emotional evolution. A series of simulations is conducted to observe the characteristics of the proposed model. The results demonstrate that the proposed model can reflect the real-world situation of emotional contagion for different distributions of personality factors.

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Notes

  1. In general, the ’friend’ relationship is stronger than ’following’. Moreover, a ’friend’ relationship can be viewed as bi-directional ’following’. Therefore, the value of ω1 is usually set to be twice of ω2.

References

  1. Bergstrom CT, Hill AL, Rand DG, Nowak MA, Christakis NA (2010) Infectious disease modeling of social contagion in networks. PLos Computational Biology 6(11)

  2. Bollen J, Gonçalves B, Ruan G, Mao H (2011) Happiness is assortative in online social networks. Artif Life 17(3):237–251

    Article  Google Scholar 

  3. Bond RM, Fariss CJ, Jones JJ, Kramer ADI, Marlow C, Settle JE, Fowler JH (2012) A 61-million-person experiment in social influence and political mobilization. Nature 489(7415):295–298

    Article  Google Scholar 

  4. Bosse T, Duell R, Memon ZA, Treur J, Van Der Wal CN (2009) Multi-agent model for mutual absorption of emotions. ECMS 2009:212–218

    Article  Google Scholar 

  5. Bozorgi A, Samet S, Kwisthout J, Wareham T (2017) Community-based influence maximization in social networks under a competitive linear threshold model. Knowl-Based Syst 134:149–158

    Article  Google Scholar 

  6. Budak C, Agrawal D, El Abbadi AE (2011) Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International conference on world wide web, pp 665–674

  7. Corradini E, Nocera A, Ursino D, Virgili L (2021) Investigating negative reviews and detecting negative influencers in yelp through a multi-dimensional social network based model, Int J Inf Manag 60(C). https://doi.org/10.1016/j.ijinfomgt.2021.102377

  8. Du Z, Yang Y, Cai Q, Zhang C, Bai Y (2018) Modeling and inferring mobile phone users’ negative emotion spreading in social networks. Futur Gener Comput Syst 78:933–942

    Article  Google Scholar 

  9. Fan R, Xu K, Zhao J (2018) An agent-based model for emotion contagion and competition in online social media. Phys A: Stat Mech Appl 495:245–259

    Article  Google Scholar 

  10. Fu L, Song W, Lv W, Lo S (2014) Simulation of emotional contagion using modified sir model: a cellular automaton approach. Phys A: Stat Mech Appl 405:380–391

    Article  Google Scholar 

  11. Goldberg LR (1990) An alternative “description of personality”: The big-five factor structure. J Pers Soc Psychol 59(6):1216–1229

    Article  Google Scholar 

  12. Gui H, Sun Y, Han J, Brova G (2014) Modeling topic diffusion in multi-relational bibliographic information networks. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3–7, 2014, pp 649–658

  13. Guille A, Hacid H (2012) A predictive model for the temporal dynamics of information diffusion in online social networks. In: Mille A, Gandon FL, Misselis J, Rabinovich M, Staab S (eds) Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16–20, 2012 (Companion Volume), pp 1145–1152

  14. Gurbin T (2015) Enlivening the machinist perspective: Humanising the information processing theory with social and cultural influences. Procedia Soc Behav Sci 197:2331–2338

    Article  Google Scholar 

  15. Hill AL, Rand DG, Nowak MA, Christakis NA (2010) Emotions as infectious diseases in a large social network: the sisa model. Proc R Soc B Biol Sci 277(1701):3827–3835

    Article  Google Scholar 

  16. Kim DJ, Salehan M (2015) The effect of sentiment on information diffusion in social media. In: 21st americas conference on information systems, AMCIS 2015, puerto rico, august 13–15

  17. Leskovec J, Mcauley J (2012) Learning to discover social circles in ego networks, pp 539–547

  18. Lhommet M, Lourdeaux D, Barthes JA (2011) Never alone in the crowd: A microscopic crowd model based on emotional contagion. Web Intell 2:89–92

    Google Scholar 

  19. Lin S, Wang F, Hu Q, Yu PS (2013) Extracting social events for learning better information diffusion models. In: The 19th international conference on knowledge discovery and data mining, pp 365–373. ACM

  20. Liu Z, Jin W, Huang P, Chai Y (2013) An emotion contagion simulation model for crowd events. J Comput Res Develop 50(12):2578–2589

    Google Scholar 

  21. Marsella SC, Gratch J (2009) Ema: a process model of appraisal dynamics. Cogn Syst Res 10(1):70–90

    Article  Google Scholar 

  22. Mehrabian A (1996) Analysis of the big-five personality factors in terms of the pad temperament model. Aust J Psychol 48(2):86–92

    Article  Google Scholar 

  23. Mehrabian A (1996) Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psychol 14 (4):261–292

    Article  MathSciNet  Google Scholar 

  24. Ortony A, Clore GL, Collins A (1990) The cognitive structure of emotions. Cambridge University Press

  25. Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: International conference on knowledge based and intelligent information and engineering systems, pp 67–75

  26. Song Z, Shi R, Jia J, Wang J (2016) Sentiment contagion based on the modified sosa-spsa model. Comput Math Methods Med 2016(5):1–7

    Article  Google Scholar 

  27. Stieglitz S, Dang-xuan L (2013) Emotions and information diffusion in social media: Sentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–247

    Article  Google Scholar 

  28. Vries DAD, Möller AM, Wieringa MS, Eigenraam AW, Hamelink K (2017) Social comparison as the thief of joy: Emotional consequences of viewing strangers’ instagram posts. Media Psychol 21(2):1–24

    Google Scholar 

  29. Wang Q, Lin Z, Jin Y, Cheng S, Yang T (2015) Esis: Emotion-based spreader-ignorant-stifler model for information diffusion. Knowl-Based Syst 81:46–55

    Article  Google Scholar 

  30. Xiong X, Li Y, Qiao S, Han N, Wu Y, Peng J, Li B (2018) An emotional contagion model for heterogeneous social media with multiple behaviors. Phys A: Stat Mech Appl 490:185–202

    Article  MathSciNet  Google Scholar 

  31. Zhao L, Wang J, Huang R, Cui H, Qiu X, Wang X (2014) Sentiment contagion in complex networks. Phys A: Stat Mech Appl 394:17–23

    Article  Google Scholar 

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Correspondence to Zhen Liu.

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Hung, CC., Gao, X., Liu, Z. et al. CECM: A cognitive emotional contagion model in social networks. Multimed Tools Appl 83, 1001–1023 (2024). https://doi.org/10.1007/s11042-023-15394-x

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