Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

The Sociology of Emotions on Digital Platforms: Attention Economy, Emotion Management, and Emotional Exhaustion among Indian Users

Document Type : Original Article

Authors
I. Gupta
V. Singh
Department of Sociology, Faculty of Social Sciences, Christ University, Bengaluru, India
10.22034/tdas.2025.255758
Abstract
Digital platforms derive economic value from user attention, and emotionally charged expression is among the most effective means of attracting it. Drawing on Hochschild's sociology of emotion management, attention-economy theory, and Bourdieu's field theory, this study examines how users' engagement with attention-economy logics is associated with emotion management, emotional ambivalence, and emotional exhaustion in India, a setting in which everyday platform use spans domestic platforms and global ones under an active regime of app blocking and internet shutdowns. A cross-sectional online survey was completed by 384 active users aged 18–45, recruited through convenience and snowball sampling. Data were analyzed using Pearson correlations, one-way ANOVA, independent-samples t-tests, and structural equation modeling. Mean scores for the attention economy (M = 3.80) and emotion management (M = 3.90) were above the midpoint of the five-point scale. The attention economy was strongly correlated with emotion management (r = .68), and emotional ambivalence was correlated with emotional exhaustion (r = .59). In the structural model, the attention economy predicted emotion management (β = .71), emotion management predicted emotional ambivalence (β = .65), and emotional ambivalence predicted emotional exhaustion (β = .57); a smaller direct path from the attention economy to emotional ambivalence was also observed (β = .22), and the serial indirect association of the attention economy with emotional exhaustion through emotion management and emotional ambivalence was significant (β = .26). Instagram users reported higher emotion management than Telegram users, and women reported more digital emotional labor than men. The findings are consistent with the argument that platforms convert emotional expression into an attention-generating resource, thereby extending emotion management from the workplace into everyday digital life, with psychological costs that are unevenly distributed across platforms and genders. Because the design is cross-sectional and non-probabilistic, the associations should not be interpreted as causal effects.
Keywords
Sociology of Emotions
Attention Economy
Emotion Management
Digital Emotional Labor
Emotional Ambivalence
Emotional Exhaustion
Digital Platforms
India
[1]  Steinert, S., & Dennis, M. J. (2022). Emotions and digital well-being: On social media's emotional affordances. Philosophy & Technology, 35(2), Article 36. https://doi.org/10.1007/s13347-022-00530-6
[2]       Simon, H. A. (1971). Designing organizations for an information-rich world. In M. Greenberger (Ed.), Computers, communications, and the public interest (pp. 37–72). Johns Hopkins University Press.
[3]       Goldhaber, M. H. (1997). The attention economy and the net. First Monday, 2(4). https://doi.org/10.5210/fm.v2i4.519
[4]       Davenport, T. H., & Beck, J. C. (2001). The attention economy: Understanding the new currency of business. Harvard Business School Press.
[5]       Heitmayer, M. (2025). The second wave of attention economics: Attention as a universal symbolic currency on social media and beyond. Interacting with Computers, 37(1), 18–29. https://doi.org/10.1093/iwc/iwae035
[6]       Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353
[7]       Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. https://doi.org/10.1073/pnas.1618923114
[8]       Nielsen, L. Y., Krøjer, J., & Nielsen, M. L. (2025). The entanglement of emotional labour and digital work platforms: A study of how professional gamers, influencers, and gig and crowd workers experience the affordances of digital work platforms. MedieKultur: Journal of Media and Communication Research, 41(78). https://doi.org/10.7146/mk.v40i78.137268
[9]       Matz, R. (2022). Art and affect, the emotional labor of the Instagram follower [Master's thesis, University of Toronto]. TSpace. https://tspace.library.utoronto.ca/bitstream/1807/124995/2/Matz_Robert_202211_MIS_thesis.pdf
[10]     Hülsheger, U. R., & Schewe, A. F. (2011). On the costs and benefits of emotional labor: A meta-analysis of three decades of research. Journal of Occupational Health Psychology, 16(3), 361–389. https://doi.org/10.1037/a0022876
[11]     Freedom House. (2025). India: Freedom on the Net 2025. https://freedomhouse.org/country/india/freedom-net/2025
[12] Punathambekar, A., & Mohan, S. (Eds.). (2019). Global digital cultures: Perspectives from South Asia. University of Michigan Press. https://library.oapen.org/handle/20.500.12657/23985
[13]     McAlister, K. L., Beatty, C. C., Smith-Caswell, J. E., Yourell, J. L., & Huberty, J. L. (2024). Social media use in adolescents: Bans, benefits, and emotion regulation behaviors. JMIR Mental Health, 11, Article e64626. https://doi.org/10.2196/64626
[14]     Piccerillo, L., & Digennaro, S. (2025). Adolescent social media use and emotional intelligence: A systematic review. Adolescent Research Review, 10(2), 201–218. https://doi.org/10.1007/s40894-024-00245-z
[15]     Giraldo-Luque, S., Aldana Afanador, P. N., & Fernández-Rovira, C. (2020). The struggle for human attention: Between the abuse of social media and digital wellbeing. Healthcare, 8(4), Article 497. https://doi.org/10.3390/healthcare8040497
[16]     Duffy, B. E., & Hund, E. (2015). "Having it all" on social media: Entrepreneurial femininity and self-branding among fashion bloggers. Social Media + Society, 1(2). https://doi.org/10.1177/2056305115604337
[17]     Hochschild, A. R. (1979). Emotion work, feeling rules, and social structure. American Journal of Sociology, 85(3), 551–575. https://doi.org/10.1086/227049
[18]     Hochschild, A. R. (1983). The managed heart: Commercialization of human feeling. University of California Press.
[19]     Hochschild, A. R. (2012). The outsourced self: Intimate life in market times. Metropolitan Books.
[20]     Nam, T., & Kabutey, R. (2021). How does social media use influence the relationship between emotional labor and burnout? The case of public employees in Ghana. Journal of Global Information Management, 29(4), 32–52. https://doi.org/10.4018/JGIM.20210701.oa2
[21]     Andrejevic, M. (2019). Automated media. Routledge. https://doi.org/10.4324/9780429242595
[22]     Zulli, D., & Zulli, D. J. (2022). Extending the Internet meme: Conceptualizing technological mimesis and imitation publics on the TikTok platform. New Media & Society, 24(8), 1872–1890. https://doi.org/10.1177/1461444820983603
[23]     Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press.
[24]     Reay, D. (2004). Gendering Bourdieu's concepts of capitals? Emotional capital, women and social class. The Sociological Review, 52(2_suppl), 57–74. https://doi.org/10.1111/j.1467-954X.2005.00524.x
[25]     Nye, J. S. (2004). Soft power: The means to success in world politics. PublicAffairs.
[26]     Marwick, A. E., & boyd, d. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133. https://doi.org/10.1177/1461444810365313
[27]     Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.
[28]     Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
[29]     Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
[30]     Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
[31]     Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
[32]     Taylor, A. B., MacKinnon, D. P., & Tein, J.-Y. (2008). Tests of the three-path mediated effect. Organizational Research Methods, 11(2), 241–269. https://doi.org/10.1177/1094428107300344
[33]        Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723
Transactions on Data Analysis in Social Science
Volume 7, Issue 4
Autumn 2025
Pages 256-271

  • Receive Date 02 April 2025
  • Revise Date 20 July 2025
  • Accept Date 09 August 2025