Skip to main content

Advertisement

SpringerLink
Log in

Predicting happiness contagion on online social networks

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In the real world, emotions guide the minds of human beings, which further influences their behaviors. During the recent years, with the increasing popularity of the internet, Online Social Networks (OSNs) have been attracting increasing number of users. It is confirmed that the emotional contagion phenomenon exists in OSNs similar to the real world. This was mostly studied through texts in current researches. Apart from the texts, however, OSNs provide their users several other interaction functions. To understand whether these interaction functions generate various levels of emotional influences to their users, this study investigates Facebook users, through interactions including the posts, number of likes, number of shares, number of fans, and number of comments, to speculate the relevant levels of user happiness. Furthermore, we propose an algorithm to identify the top-n users with the most happiness influences. According to the experimental results, among the Facebook users, various interactions generate different levels of happiness influences on members in its community. The members with a higher level of happiness are also better known in the community. In addition, we experimentally confirm that the proposed algorithm can effectively determine the top-n users of the most happiness influences.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. https://www.paradigmagency.com/.

  2. https://developers.facebook.com/docs/graph-api/.

References

  1. Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science. https://doi.org/10.1126/science.1215842

  2. Barsade SG (2002) The ripple effect: Emotional contagion and its influence on group behavior. Adm Sci Q. https://doi.org/10.2307/3094912

  3. Bollen J, Goncalves B, Ruan G, Mao H (2011) Happiness is assortative in online social networks. ArXiv: 1103.0784

  4. Borgatti SP (2005) Centrality and network flow. Soc Netw. https://doi.org/10.1016/j.socnet.2004.11.008

  5. Chakhmakhchyan L, Shepelyansky D (2013) PageRank model of opinion formation on Ulam networks. Phys Lett A. https://doi.org/10.1016/j.physleta.2013.10.003

  6. Coviello L, Sohn Y, Kramer ADI, Marlow C, Franceschetti M, Christakis NA, Fowler JH (2014) Detecting emotional contagion in massive social networks. PLoS One. https://doi.org/10.1371/journal.pone.0090315

  7. Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS One. https://doi.org/10.1371/journal.pone.0026752

  8. Fornito A, Zalesky A, Bullmore ET (2016) Centrality and hubs. Fundamentals of Brain Network Analysis

  9. Frey BS, Stutzer A, Benz M, Meier S, Luechinger S, Benesch C (2008) Happiness: A revolution in economics. MIT Press, Cambridge

  10. Gómez D, Figueira JR, Eusébio A (2013) Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2012.11.027

  11. Hatfield E, Cacioppo JT, Rapson RL (1993) Emotional contagion. Cambridge University Press, Cambridge

  12. Kramer ADI, Guillory J, Hancock JT (2014) Experimental evidence of massive-scale emotional contagion through social networks. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1320040111

  13. Meghanathan N (2018) δ-Space for real-world networks: A correlation analysis of decay centrality vs. degree centrality and closeness centrality. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.04.006

  14. Metz M, Kruikemeier S, Lecheler S (2019) Personalization of politics on Facebook: Examining the content and effects of profession motional and private self-personalization. Inf Commun Soc. https://doi.org/10.1080/1369118X.2019.1581244

  15. Nireshwalya S, Basava A, Bhattacharya S (2018) Influence maximization in large social networks: Heuristics, models and parameters. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2018.07.015

  16. Nykl M, Campr M, Ježek K (2015) Author ranking based on personalized PageRank. J Informetr. https://doi.org/10.1016/j.joi.2015.07.002

  17. Scherer KR (2005) What are emotions? And how can they be measured? Soc Sci Inf. https://doi.org/10.1177/0539018405058216

  18. Seligman MEP (2002) Authentic happiness: Using the new positive psychology to realize your potential for lasting fulfillment. Simon and Schuster, New York

  19. Sharara H, Getoor L, Norton M (2011) Active surveying: A probabilistic approach for identifying key opinion leaders. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence

  20. Sharifirad S, Jafarpour B, Matwin S (2019) How is your mood when writing sexist tweets? Detecting the emotion type and intensity of emotion using natural language processing techniques. ArXiv: 1902.03089

  21. Shugars S, Beauchamp N (2019) Why keep arguing? Predicting engagement in political conversations online. SAGE Open. https://doi.org/10.1177/2158244019828850

  22. Tang J, Zhang Y, Sun J, Rao J, Yu W, Chen Y, Fong ACM (2012) Quantitative study of individual emotional states in social networks. IEEE Trans Affect Comput. https://doi.org/10.1109/T-AFFC.2011.23

  23. Tang D, Zhang Z, He Y, Lin C, Zhou D (2019) Hidden topic–emotion transition model for multi-level social emotion detection. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2018.11.014

  24. Tsiotas D, Polyzos S (2015) Introducing a new centrality measure from the transportation network analysis in Greece. Ann Oper Res. https://doi.org/10.1007/s10479-013-1434-0

  25. Yustiawan Y, Maharani W, Gozali AA (2015) Degree centrality for social network with Opsahl Method. Procedia Comput Sci. https://doi.org/10.1016/J.PROCS.2015.07.559

  26. Zhang X, Li W, Huang H, Nguyen C-T, Chen X, Wang X, Lu S (2017) Predicting happiness state based on emotion representative mining in online social networks. In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments. In addition, this work was supported by the Ministry of Science and Technology of Republic of China under grant MOST 107-2221-E-025-008, MOST 108-2221-E-025-007, and MOST 109-2221-E-025-012.

Author information

Authors and Affiliations

  1. Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan

    Chen-Yi Lin & Yueh-Lun Li

Authors
  1. Chen-Yi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yueh-Lun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen-Yi Lin.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, CY., Li, YL. Predicting happiness contagion on online social networks. Multimed Tools Appl 82, 2821–2838 (2023). https://doi.org/10.1007/s11042-022-11989-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-11989-y

Keywords

Search

Navigation