Authenticity and Emotion in the Discourse of Female Influencers on Mental Health on Instagram
DOI:
https://doi.org/10.62008/ixc/16/02AutentKeywords:
Social Media, Mental Health, Influencers, Emotions, Artificial Intelligence, Natural Language ProcessingAbstract
This study explores how authenticity and emotional tone shape audience responses to mental health disclosures by Spanish influencers on Instagram. Using Artificial Intelligence and Natural Language Processing tools, 13,407 user comments were analyzed to classify emotions into six categories: admiration, empathy, anger, sadness, neutrality, and gratitude. Descriptive and correspondence analyses identified dominant emotional patterns and their associations with post types—personal, unrelated, or promotional. Results show that authentic self-disclosures generate predominantly positive emotions (admiration and empathy), whereas negative reactions (anger and mockery) are linked to promotional content. Authenticity and coherence between text and image emerged as key elements fostering user engagement. These findings contribute to understanding emotional dynamics in digital communication on mental health and highlight the potential of AI tools to analyze public discourse online.
References
Adeane E., & Stasiak K. (2024). It’s really hard to strike a balance: The role of digital influencers in shaping youth mental health. Digital Health, 10. https://doi.org/10.1177/20552076241288059
Al-Rawi, A. (2017). Viral News on Social Media. Digital Journalism, 7(1), 63–79. https://doi.org/10.1080/21670811.2017.1387062
Alhussein, G., Ziogas, I., Saleem, S., & Hadjileontiadis, L. J. (2025). Speech emotion recognition in conversations using artificial intelligence: a systematic review and meta-analysis. Artificial Intelligence Review, 58, 198. https://doi.org/10.1007/s10462-025-11197-8
Alvarez-Mon, M. A., Donat-Vargas, C., Santoma-Vilaclara, J., Anta, L. de, Goena, J., Sanchez-Bayona, R., Mora, F., Ortega, M. A., Lahe-ra, G., Rodriguez-Jimenez, R., Quintero, J., & Álvarez-Mon, M. (2021). As-sessment of Antipsychotic Medications on Social Media: Machine Learn-ing Study. Frontiers in Psychiatry, 12, 14. https://doi.org/10.3389/FPSYT.2021.737684
Arcila Calderón, C., Van Atteveldt, W., & Trilling, D. (2021). Métodos Computacionales y Big Data en la Investigación en Comunicación: Edito-rial Dossier. Cuadernos.Info, 49, I-IV. https://doi.org/10.7764/cdi.49.35333
Audrezet, A., De Kerviler, G., & Moulard, J. G. (2020). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 117, 557–569. https://doi.org/10.1016/j.jbusres.2018.07.008
Baquerizo-Neira, M. G., Labate, C., Zerega Garaycoa, M. M., Cisternas Osorio, R., & Becker Cantariño, L. M. (2025). Identidad digital y co-municación de contenidos de influencers del bienestar emocional his-panoamericanos. Revista Española De Comunicación En Salud, 16(1), 89-105. https://doi.org/10.20318/recs.2025.9315
Benton, M., Wittkowski, A., Edge, D., Reid, H. E., Quigley, T., Sheikh, Z., & Smith, D. M. (2024). Best practice recommendations for the integra-tion of trauma-informed approaches in maternal mental health care within the context of perinatal trauma and loss: A systematic review of current guidance. Midwifery, 131, 103949. https://doi.org/https://doi.org/10.1016/j.midw.2024.103949
Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Jour-nal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353
Bernal-Delgado, E., Angulo-Pueyo, E., Ridao-López, M., Urbanos-Garrido, R. M., Oliva-Moreno, J., García-Abiétar, D., & Hernández-Quevedo, C. (2024). Spain: health system review 2024. Health Systems in Transition, 26(3), 1-187. https://tinyurl.com/mpjwjm8j
Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017). A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research, 19(8), e7956. https://doi.org/10.2196/jmir.7956
Bograd, S., Chen, B., & Kavuluru, R. (2022). Tracking sentiments toward fat acceptance over a decade on Twitter. Health Informatics Journal, 28(1), 14604582211065702. https://doi.org/10.1177/14604582211065702
Budenz, A., Klassen, A., Purtle, J., Yom Tov, E., Yudell, M., & Massey, P. (2020). Mental illness and bipolar disorder on Twitter: implications for stigma and social support. Journal of Mental Health, 29(2), 191–199. https://doi.org/10.1080/09638237.2019.1677878
Cervantes Olivera, J. (2025). El impacto de las redes sociales en las emociones de los adolescentes de 12 a 18 años: sentido de pertenencia. Ciencia Y Reflexión, 4(2), 920–945. https://doi.org/10.70747/cr.v4i2.332
Chang, Y. P., Lin, Y. C., & Chen, L. H. (2012). Pay it forward: Gratitude in SNs. Journal of Happiness Studies, 13(5), 761-781. https://doi.org/10.1007/s10902-011-9289-z
Confederación Salud Mental España y Mutua Madrileña. (2023). La situación de la salud mental en España. https://tinyurl.com/y6zc7mkh
Connelly, L. (2019). Chi-Square Test. Medsurg Nursing, 28(2), 127. https://tinyurl.com/4efezw95
Datareportal. (2024). Digital 2024: Spain. DataReportal global digital se-ries. https://tinyurl.com/ycvdjch9
Francis, D. B. (2021). Twitter is Really Therapeutic at Times: Examination of Black Men’s Twitter Conversations Following Hip-Hop Artist Kid Cudi’s Depression Disclosure. Health Communication, 36(4), 448–456. https://doi.org/10.1080/10410236.2019.1700436
Franssen, G. (2019). The celebritization of self-care: The celebrity health narrative of Demi Lovato and the sickscape of mental illness. European Journal of Cultural Studies, 23(1), 89–111. https://doi.org/10.1177/1367549419861636
Fundación AXA (2023). Estudio internacional de salud mental AXA España. https://tinyurl.com/3x5d5ncf
Gronholm, P. C., & Thornicroft, G. (2022). Impact of celebrity disclosure on mental health-related stigma. Epidemiology and psychiatric sciences, 31, e62. https://doi.org/10.1017/S2045796022000488
Ibáñez-Hernández, A. y Carretón-Ballester C. (2025). La visibilidad de la epidemia en Instagram: el rol de los influencers como creadores de contenido en salud. index.comunicación, 15(2), 83-108. https://doi.org/10.62008/ixc/15/02Lavisi
Jilka, S., Odoi, C. M., Van Bilsen, J., Morris, D., Erturk, S., Cummins, N., Cella, M., & Wykes, T. (2022). Identifying schizophrenia stigma on Twit-ter: a proof of principle model using service user supervised machine learning. Schizophrenia, 8(1), 1. https://doi.org/10.1038/s41537-021-00197-6
Khan, A., Ali, R. (2024). Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media. Social Network Analysis and Mining, 14(78) https://doi.org/10.1007/s13278-024-01205-0
Lee, Y. H., Yuan, C. W., & Wohn, D. Y. (2021). How Video Streamers’ Mental Health Disclosures Affect Viewers’ Risk Perceptions. Health Communica-tion, 36(14), 1931–1941. https://doi.org/10.1080/10410236.2020.1808405
Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of market-ing re-search, 57(1), 1-19. https://doi.org/10.1177/0022243719881113
Lindgren, S. (2025). Digital Media and Society, 3rd Edition. Sage.
López-de-Ayala, M.C., & Díaz-Lucena, A. (2025). Instagram como Platafor-ma para la Salud Mental: evolución de las estrategias de publicación y com-promiso. Miguel Hernández Communication Journal, 16 (2), 467-484. https://doi.org/10.21134/tkb48j06
López-Ubeda, P., Del Arco, F. M. P., Galiano, M. C. D., Lopez, L. A. U., & Martín-Valdivia, M. T. (2019). Detecting anorexia in Spanish tweets. In Proceedings of Recent Advances in Natural Language Processing, pages 655–663, Varna, Bulgaria, Sep 2–4, 2019. https://doi.org/10.26615/978-954-452-056-4_077
Luo, M., & Hancock, J. T. (2020). Self-disclosure and social media: motiva-tions, mechanisms and psychological well-being. Current opinion in psy-chology, 31, 110-115. https://doi.org/10.1016/j.copsyc.2019.08.019
Malhotra, A., & Jindal, R. (2022). Deep learning techniques for suicide and de-pression detection from online social media: A scoping review. Ap-plied Soft Computing, 130, 109713. https://doi.org/10.1016/j.asoc.2022.109713
Mardon, R., Cocker, H., & Daunt, K. (2023). When parasocial relationships turn sour: Social media influencers, eroded and exploitative intimacies, and anti-fan communities. Journal of Marketing Management, 39(11-12), 1132-1162. https://doi.org/10.1080/0267257X.2022.2149609
Marwick, A. E. (2015). Instafame: Luxury selfies in the attention economy. Public culture, 27(1(75)), 137-160. https://doi.org/10.1215/08992363-2798379
Nelson, A. (2019). Ups and Downs: Social Media Advocacy of Bipolar Disor-der on World Mental Health Day. Frontiers in Communication, 4. https://doi.org/10.3389/fcomm.2019.00024
Oscar, N., Fox, P. A., Croucher, R., Wernick, R., Keune, J., & Hooker, K. (2017). Machine Learning, Sentiment Analysis, and Tweets: An Examina-tion of Alzheimer’s Disease Stigma on Twitter. The Journals of Gerontolo-gy: Series B, 72(5), 742–751. https://doi.org/10.1093/geronb/gbx014
Ouvrein, G. (2025). Followers, fans, friends, or haters? A typology of the online interactions and relationships between social media influencers and their audiences based on a social capital framework. New Media & Society, 27(10), 5659-5690. https://doi.org/10.1177/14614448241253770
Pande, S.D., Hasane Ahammad, S.K., Gurav, M.N., Faragallah, O.S., Eid, M. M. A., & Rashed A.N.Z. (2024). Depression detection based on social networking sites using data mining. Multimedia Tools Applied, 83, 25951–25967. https://doi.org/10.1007/s11042-023-16564-7
Papacharissi, Z. (2014). Affective Publics: Sentiment, Technology, and Poli-tics. Oxford University Press. New York. https://doi.org/10.1093/acprof:oso/9780199999736.001.0001.
Pendleton S. M. (2023). Exploring Stigma and Black Mental Health on Twit-ter. Journal of health communication, 28(7), 425–435. https://doi.org/10.1080/10810730.2023.2218288
Pérez-Castaños, S., Antón-Merino, J., & García-Santamaría, S. (2023). Emociones, liderazgo y redes sociales. Propuesta para su medición en materiales de campaña electoral. Revista Española De Investigaciones So-ciológicas, (184). https://doi.org/10.5477/cis/reis.184.125
Sánchez Castillo, S., Armayones Ruiz, M. y Meléndez-Labrador, S. (2025). La información sobre salud en la esfera digital. in-dex.comunicación, 15(2), 13-32. https://doi.org/10.62008/ixc/15/02Lainfo
Sun, Y., Wu, Y., Fan, S., Dal Santo, T., Li, L., Jiang, X., Li, K., Wang, Y., Tasleem, A., Krishnan, A., He, C., Bonardi, O., Boruff, J. T., Rice, D. B., Markham, S., Levis, B., Azar, M., Thombs-Vite, I., Neupane, D., … Thombs, B. D. (2023). Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts. BMJ, 380, e074224. https://doi.org/10.1136/bmj-2022-074224
Tudehope, L., Harris, N., Vorage, L., & Sofija, E. (2024). What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychology, 12(1), 105. https://doi.org/10.1186/s40359-024-01603-1
Valenzuela-García, N., Maldonado-Guzmán, D. J., García-Pérez, A., & Del-Real, C. (2023). Too lucky to be a victim? an exploratory study of online harassment and hate messages faced by social media influencers. European journal on criminal policy and research, 29(3), 397-421. https://doi.org/10.1007/s10610-023-09542-0
Van Dijk, T. A. (2016). Discourse and Knowledge. Cambridge University Press.
Vasha, Z. N., Sharma, B., Esha, I. J., Al Nahian, J., & Polin, J. A. (2023). Depression detection in social media comments data using machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 12(2), 987–996. https://doi.org/10.11591/eei.v12i2.4182
Withers, M., Jahangir, T., Kubasova, K., & Ran, M.-S. (2021). Reducing stigma associated with mental health problems among university stu-dents in the Asia-Pacific: A video content analysis of student-driven pro-posals. International Journal of Social Psychiatry, 68(4), 827–835. https://doi.org/10.1177/00207640211007511
Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2021). Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Al-go-rithm Development and Validation Study. JMIR Ment Health, 8(8), e19824. https://doi.org/10.2196/19824
Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twit-ter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research, 22(11), e20550. https://doi.org/10.2196/20550
Zerega Garaycoa, M.M., Tutivén-Román, C., Cisternas-Osorio, E., Labate, C. & Becker Cantariño, L.M. (2024). Spanish-speaking wellness influencers in the era of care: trends and topics in 2023. Vivat Academia, 157, 1-25. http://doi.org/10.15178/va.2024.157.e1533
Zhang, Z., Reavley, N., Armstrong, G., & Morgan, A. (2025). Public disclo-sures of mental health problems on social media and audiences’ self-reported anti-stigma effects. Health Promotion International, 40(1). https://doi.org/10.1093/heapro/daae204
Zsila, Á., Orosz, G., McCutcheon, L. E., & Demetrovics, Z. (2021). Indi-vidual Differences in the Association Between Celebrity Worship and Sub-jec-tive Well-Being: The Moderating Role of Gender and Age. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.651067
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