IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v175y2024ics0148296324000456.html
   My bibliography  Save this article

Playing with emotions: Text analysis of emotional tones in gender-casted Children’s media

Author

Listed:
  • Setten, Eric
  • Chen, Steven

Abstract

This research examines the differences in emotional tones and drives in gender-casted (e.g. boy-directed vs. girl-directed) children’s media and how this has changed over time. This topic is important given that children spend copious amounts of time watching media. Two studies utilizing Linguistic Inquiry and Word Count (LIWC) text analysis on a diverse body of over 1000 h of media transcripts from 78 franchises demonstrate that girl-directed children’s media has a more positive emotional tone overall than boy-directed media, but that this difference is narrowing over time. Additionally, boy-directed media features a greater prevalence of words expressing the emotion of anger and the drives of power and risk. In contrast, girl-directed media features a greater prevalence of words expressing the emotion of sadness and the drive of affiliation. The results proffer insights into how emotional tones and drives represented in children’s media may impact young consumers’ understanding of gender.

Suggested Citation

  • Setten, Eric & Chen, Steven, 2024. "Playing with emotions: Text analysis of emotional tones in gender-casted Children’s media," Journal of Business Research, Elsevier, vol. 175(C).
  • Handle: RePEc:eee:jbrese:v:175:y:2024:i:c:s0148296324000456
    DOI: 10.1016/j.jbusres.2024.114541
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296324000456
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2024.114541?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cordelia Fine & Emma Rush, 2018. "“Why Does all the Girls have to Buy Pink Stuff?” The Ethics and Science of the Gendered Toy Marketing Debate," Journal of Business Ethics, Springer, vol. 149(4), pages 769-784, June.
    2. Ashlee Humphreys & Rebecca Jen-Hui Wang & Eileen FischerEditor & Linda PriceAssociate Editor, 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1274-1306.
    3. Benjamin Hine & Katarina Ivanovic & Dawn England, 2018. "From the Sleeping Princess to the World-Saving Daughter of the Chief: Examining Young Children’s Perceptions of ‘Old’ versus ‘New’ Disney Princess Characters," Social Sciences, MDPI, vol. 7(9), pages 1-15, September.
    4. Greco, Francesca & Polli, Alessandro, 2020. "Emotional Text Mining: Customer profiling in brand management," International Journal of Information Management, Elsevier, vol. 51(C).
    5. Schiele, Kristen & Louie, Lauren & Chen, Steven, 2020. "Marketing feminism in youth media: A study of Disney and Pixar animation," Business Horizons, Elsevier, vol. 63(5), pages 659-669.
    6. D. H. Skuse & R. S. James & D. V. M. Bishop & B. Coppin & P. Dalton & G. Aamodt-Leeper & M. Bacarese-Hamilton & C. Creswell & R. McGurk & P. A. Jacobs, 1997. "Evidence from Turner's syndrome of an imprinted X-linked locus affecting cognitive function," Nature, Nature, vol. 387(6634), pages 705-708, June.
    7. Tahir Mahmood & Urwah Iftikhar & Muhammad Ahsan Bhatti, 2020. "Impact of Violent Cartoons on the Behaviour of Children: A Case Study of South Punjab," Journal of Business and Social Review in Emerging Economies, CSRC Publishing, Center for Sustainability Research and Consultancy Pakistan, vol. 6(2), pages 689-702, June.
    8. Meier-Pesti, Katja & Penz, Elfriede, 2008. "Sex or gender? Expanding the sex-based view by introducing masculinity and femininity as predictors of financial risk taking," Journal of Economic Psychology, Elsevier, vol. 29(2), pages 180-196, April.
    9. Mike Thelwall & David Wilkinson & Sukhvinder Uppal, 2010. "Data mining emotion in social network communication: Gender differences in MySpace," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 190-199, January.
    10. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    11. Borau, Sylvie & Bonnefon, Jean-François, 2020. "Gendered products act as the extended phenotype of human sexual dimorphism: They increase physical attractiveness and desirability," Journal of Business Research, Elsevier, vol. 120(C), pages 498-508.
    12. Michael Macaluso, 2018. "Postfeminist Masculinity: The New Disney Norm?," Social Sciences, MDPI, vol. 7(11), pages 1-10, November.
    13. Mike Thelwall & David Wilkinson & Sukhvinder Uppal, 2010. "Data mining emotion in social network communication: Gender differences in MySpace," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 190-199, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tian Tian & Stijn Speelman, 2021. "Pursuing Development behind Heterogeneous Ideologies: Review of Six Evolving Themes and Narratives of Rural Planning in China," Sustainability, MDPI, vol. 13(17), pages 1-16, September.
    2. Gandhi, Mohina & Kar, Arpan Kumar, 2022. "How do Fortune firms build a social presence on social media platforms? Insights from multi-modal analytics," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Cassandra Primo, 2018. "Balancing Gender and Power: How Disney’s Hercules Fails to Go the Distance," Social Sciences, MDPI, vol. 7(11), pages 1-13, November.
    4. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    5. Liwen Vaughan, 2016. "Uncovering information from social media hyperlinks: An investigation of twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1105-1120, May.
    6. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 2017. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 17(1), pages 101-119, March.
    7. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    8. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    9. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    10. Feng, Cong & Fay, Scott, 2022. "An empirical investigation of forward-looking retailer performance using parking lot traffic data derived from satellite imagery," Journal of Retailing, Elsevier, vol. 98(4), pages 633-646.
    11. Katsumata, Sotaro & Nishimoto, Akihiro & Kannan, P.K., 2023. "Brand competitiveness and resilience to exogenous shock: Usage of smartphone apps during the COVID-19 pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    12. Li, Xianghua & Wang, Zhen & Gao, Chao & Shi, Lei, 2017. "Reasoning human emotional responses from large-scale social and public media," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 182-193.
    13. Anupriya Khan & Satish Krishnan & Jithesh Arayankalam, 2022. "The Role of ICT Laws and National Culture in Determining ICT Diffusion and Well-Being: A Cross-Country Examination," Information Systems Frontiers, Springer, vol. 24(2), pages 415-440, April.
    14. Ibtesam AbdulAziz Bajri & Nada Abdulmajeed Lashkar, 2020. "Saudi Gender Emotional Expressions in Using Instagram," English Language Teaching, Canadian Center of Science and Education, vol. 13(5), pages 1-94, May.
    15. Roser Beneito-Montagut, 2017. "Emotions, Everyday Life, and the Social Web: Age, Gender, and Social Web Engagement Effects on Online Emotional Expression," Sociological Research Online, , vol. 22(4), pages 87-104, December.
    16. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 0. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 0, pages 1-19.
    17. Jacqueline Ng Lane & Bruce Ankenman & Seyed Iravani, 2018. "Insight into Gender Differences in Higher Education: Evidence from Peer Reviews in an Introductory STEM Course," Service Science, INFORMS, vol. 10(4), pages 442-456, December.
    18. Yulei Gavin Zhang & Mandy Yan Dang & Hsinchun Chen, 2020. "An Explorative Study on the Virtual World: Investigating the Avatar Gender and Avatar Age Differences in their Social Interactions for Help-Seeking," Information Systems Frontiers, Springer, vol. 22(4), pages 911-925, August.
    19. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    20. Avi Rosenfeld & Sigal Sina & David Sarne & Or Avidov & Sarit Kraus, 2018. "WhatsApp usage patterns and prediction of demographic characteristics without access to message content," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 39(22), pages 647-670.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:175:y:2024:i:c:s0148296324000456. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.