Skip to main content
Log in

Emotion detection and its influence on popularity in a social network-based on the American TV series Friends

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

4.59 billion people worldwide use social networks. Over a billion new posts are uploaded every day to Facebook’s applications (Facebook, Instagram, WhatsApp). Social networks play a central role in consumption and marketing, politics, and social aspects. They affect users' decision-making processes, emotions, and behavior. Content on social media can include, for example, movies, pictures, and texts. The emotion expressed in the post content has an influence on the exposed individuals. Emotions can cause the user exposed to the content to follow another user, view more content raised by a certain user, share a post, or express support (or lack of support), thus affecting the popularity of the user. In this work, we examined the relationship between emotions expressed by a character and popularity measures. As a case study, we analyzed the texts presented in the popular series “Friends” over ten seasons. We found that women in the series express more emotions in general and, in particular, more emotions of anticipation, joy, trust, and fear. The findings show the relationship between different emotions expressed in the content and various popularity measures.

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

Similar content being viewed by others

References

  • Abbas MJ, Khalil LS, Haikal A, Dash ME, Dongmo G, Okoroha KR (2021) Eliciting emotion and action increases social media engagement: an analysis of influential orthopaedic surgeons. Arthrosc Sports Med Rehabilit 3(5):e1301–e1308

    Article  Google Scholar 

  • Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media

  • Carrasco Polaino R, Villar Cirujano E, Martín Cárdaba MÁ (2019) Redes, tweets y engagement: análisis de las bibliotecas universitarias españolas en Twitter. El Prof De La Inf 28(4):1–14

    Google Scholar 

  • Carrasco-Polaino R, Villar-Cirujano E, Martín-Cárdaba MA (2018) Artivismo y ONG: Relación entre imagen y engagement en Instagram= Artivism and NGO: relationship between image and engagement in Instagram, 29–38

  • Chakraborty K, Bhattacharyya S, Bag R (2020) A survey of sentiment analysis from social media data. IEEE Trans Comput Soc Syst 7(2):450–464

    Article  Google Scholar 

  • Csardi MG (2013) Package ‘igraph.’ Last Accessed 3(09):2013

    Google Scholar 

  • Figueiredo F, Almeida JM, Gonçalves MA, Benevenuto F (2014) On the dynamics of social media popularity: a YouTube case study. ACM Trans Internet Technol TOIT 14(4):1–23

    Article  Google Scholar 

  • Figueiredo F, Almeida JM, Gonçalves MA, Benevenuto F (2016) Trendlearner: early prediction of popularity trends of user generated content. Inf Sci 349:172–187

    Article  Google Scholar 

  • Fletcher JM, Wennekers T (2018) From structure to activity: using centrality measures to predict neuronal activity. Int J Neural Syst 28(02):1750013

    Article  Google Scholar 

  • Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  • Freeman LC (2002) Centrality in social networks: conceptual clarification. In: Scott J (ed) Social network: critical concepts in sociology, vol 1. Routledge, Londres, pp 238–263

    Google Scholar 

  • Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh international conference on contemporary computing (IC3)

  • Gundecha P, Liu H (2012) Mining social media: a brief introduction. In: New directions in informatics, optimization, logistics, and production, pp 1–17

  • Herman V (1998) Dramatic discourse: dialogue as interaction in plays. Psychology Press

    Google Scholar 

  • Iglesias CA, Moreno A (2019) Sentiment analysis for social media. In: MDPI, vol 9, p 5037

  • Kinga E (2021) How to increase engagement on Facebook: tips, tools, and tactics. Kontentino. https://www.kontentino.com/blog/how-to-increase-engagement-on-facebook/

  • Kivran-Swaine F, Brody S, Diakopoulos N, Naaman M (2012) Of joy and gender: emotional expression in online social networks. In: Proceedings of the ACM 2012 conference on computer supported cooperative work companion

  • Kocić AV, Stamenković DM, Tasić MB (2014) Gender differences reflected in friends’ conversation—discourse analysis of dialogues from the tv series coupling. Komun i Kult Online 5:52–64

    Google Scholar 

  • Kring AM, Gordon AH (1998) Sex differences in emotion: expression, experience, and physiology. J Pers Soc Psychol 74(3):686

    Article  Google Scholar 

  • Li Y, Xie Y (2020) Is a picture worth a thousand words? An empirical study of image content and social media engagement. J Mark Res 57(1):1–19

    Article  Google Scholar 

  • livesinabox (2004) Crazy for friends. http://livesinabox.com/friends/scripts.shtml

  • Lotker Z (2021) Analyzing narratives in social networks. Springer

    Book  Google Scholar 

  • Mohammad S, Turney P (2013) NRC emotion lexicon, national research council of Canada. Technical report

  • Munezero M, Montero CS, Sutinen E, Pajunen J (2014) Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Trans Affect Comput 5(2):101–111

    Article  Google Scholar 

  • Peng S, Zhou Y, Cao L, Yu S, Niu J, Jia W (2018) Influence analysis in social networks: a survey. J Netw Comput Appl 106:17–32

    Article  Google Scholar 

  • Plutchik R (1980) A general psychoevolutionary theory of emotion. In: Theories of emotion. Elsevier, pp 3–33

  • R Development Core Team (2022) R: a language and environment for statistical computing. In: R foundation for statistical computing. https://www.R-project.org/

  • Ribeiro AC, Azevedo B, Oliveira e Sá J, Baptista AA (2020) How to measure influence in social networks? In: Proceedings of the research challenges in information science: 14th international conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, 14

  • Rinker T (2019) Package ‘sentimentr’

  • Sabate F, Berbegal-Mirabent J, Cañabate A, Lebherz PR (2014) Factors influencing popularity of branded content in Facebook fan pages. Eur Manag J 32(6):1001–1011

    Article  Google Scholar 

  • Saquete E, Zubcoff J, Gutiérrez Y, Martínez-Barco P, Fernández J (2022) Why are some social-media contents more popular than others? Opinion and association rules mining applied to virality patterns discovery. Expert Syst Appl 197:116676

    Article  Google Scholar 

  • Schmidt R, Möhring M, Härting R-C, Reichstein C, Keller B (2016) Influencing factors increasing popularity on facebook–empirical insights from European Users. In: Proceedings of the 19th international conference on business information systems, BIS 2016, Leipzig, Germany, July, 6–8, 2016

  • Schreiner M, Riedl R (2019) Effect of emotion on content engagement in social media communication: a short review of current methods and a call for neurophysiological methods. Inf Syst Neurosci NeuroIS Retreat 2018:195–202

    Google Scholar 

  • Singh A, Singh RR, Iyengar S (2020) Node-weighted centrality: a new way of centrality hybridization. Comput Soc Netw 7(1):1–33

    Article  Google Scholar 

  • Statista (2023) Number of social media users worldwide from 2017 to 2027. https://www-statista-com.eu1.proxy.openathens.net/statistics/278414/number-of-worldwide-social-network-users/?locale=en

  • Statusbrew (2023) 100+ social media statistics you need to know in 2023 [all networks]. https://statusbrew.com/insights/social-media-statistics/#facebook-statistics

  • Stavrakantonakis I, Gagiu A-E, Kasper H, Toma I, Thalhammer A (2012) An approach for evaluation of social media monitoring tools. Common Value Manag 52(1):52–64

    Google Scholar 

  • Subbian K, Aggarwal CC, Srivastava J (2016) Querying and tracking influencers in social streams. In: Proceedings of the ninth ACM international conference on web search and data mining

  • Tutte WT, Tutte WT (2001) Graph theory, vol 21. Cambridge University Press, Cambridge

    Google Scholar 

  • Uros S (2020) Best content types to drive engagement on Facebook. Brid.TV. https://www.brid.tv/best-content-types-to-drive-engagement-on-facebook/#

  • Valente TW, Davis RL (1999) Accelerating the diffusion of innovations using opinion leaders. Ann Am Acad Pol Soc Sci 566(1):55–67

    Article  Google Scholar 

  • Valente TW, Coronges K, Lakon C, Costenbader E (2008) How correlated are network centrality measures? Connections (toronto, Ont.) 28(1):16

    Google Scholar 

  • Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing

  • Yue L, Chen W, Li X, Zuo W, Yin M (2019) A survey of sentiment analysis in social media. Knowl Inf Syst 60:617–663

    Article  Google Scholar 

  • Zahiri SM, Choi JD (2017) Emotion detection on TV show transcripts with sequence-based convolutional neural networks. arXiv preprint arXiv:1708.04299

  • Zhang L, Peng T-Q, Zhang Y-P, Wang X-H, Zhu JJ (2014) Content or context: which matters more in information processing on microblogging sites. Comput Hum Behav 31:242–249

    Article  Google Scholar 

  • Zhang J, Luo Y (2017) Degree centrality, betweenness centrality, and closeness centrality in social network. In: 2017 2nd international conference on modelling, simulation and applied mathematics (MSAM2017)

  • Zhao F (2011) An analysis of gender differences in interruption based on the American TV series friends

Download references

Author information

Authors and Affiliations

Authors

Contributions

I.P. and B.G. collected the data and performed the analysis. R.R.G. performed the analysis, designed and directed the project; I.P., B.G., and R.R.G. wrote the manuscript.

Corresponding author

Correspondence to Roni Ramon-Gonen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Porter, I., Galam, B. & Ramon-Gonen, R. Emotion detection and its influence on popularity in a social network-based on the American TV series Friends. Soc. Netw. Anal. Min. 13, 123 (2023). https://doi.org/10.1007/s13278-023-01133-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-023-01133-5

Keywords

Navigation