Discurso sobre #SaludMental en X/Twitter: Análisis de temáticas y su impacto

Jesús Díaz-Campo; Sergio Arce-García

doi:10.65598/rps.6010

Autores

Jesús Díaz-Campo Universidad Internacional de La Rioja https://orcid.org/0000-0001-5014-8749
Sergio Arce-García Universidad Internacional de La Rioja https://orcid.org/0000-0003-0578-9787

DOI:

https://doi.org/10.65598/rps.6010

Palavras-chave:

Salud mental, X / Twitter, Engagement, análisis temático, concienciación

Resumo

Este estudio analiza el discurso sobre salud mental en X (Twitter) mediante el hashtag #saludmental en español, durante seis meses (julio-diciembre 2022), con el objetivo de identificar temas principales, su evolución temporal y el engagement generado. Se recopilaron 3.172.605 mensajes usando la API académica v2.0 y la librería AcademicTwitteR en R, centrándose en 855.408 originales. Se aplicó clustering por frecuencia de palabras y clasificación con Naive Bayes vía IA Claude 3.7 Sonnet en Julius.ai, identificando 12 temas (ej. Salud Mental General, Ansiedad y Psicología). Se analizaron patrones temporales, correlaciones de Pearson, minería de texto (MDS, k-means) y engagement (likes, retuits). Salud Mental General dominó (45,8%), seguido de Reflexiones Generales (13,6%) y Ansiedad y Psicología (11%). Pico en octubre por el Día Mundial de la Salud Mental. Experiencias Personales y Soledad y Abandono generaron mayor engagement pese al bajo volumen. Alta correlación entre Bienestar/Ansiedad (0,99); Soledad baja con otros (0,09). El contenido emocional y personal impulsa interacciones más que temas generales, confirmando estudios previos. Campañas como el Día Mundial aumentan volumen, pero no engagement sostenido. Salud mental laboral y cuidados emergen en clusters; soledad queda aislada, requiriendo estrategias específicas. La calidad emocional prima sobre cantidad para engagement efectivo. Eventos conmemorativos catalizan debate, pero necesitan continuidad. Recomendaciones: priorizar narrativas personales, integrar soledad y contextualizar en trabajo para campañas óptimas. Futuras investigaciones deben ampliar periodos.

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Biografias Autor

Jesús Díaz-Campo, Universidad Internacional de La Rioja

Depute Vice-Rector of Research, Vice-Director of the Doctoral School and Professor at Universidad Internacional of La Rioja. Previously, Director of Master in Communication and Corporate Identity and Secretary of the Research Ethics Committee at Universidad Internacional de la Rioja (2016-2019). Phd on Media Ethics. Currently interested on Social Media and Journalism, Media Ethics and Corporate Social Responsibility, Health Communication

Sergio Arce-García, Universidad Internacional de La Rioja

Profesor Titular de la Universidad Internacional de La Rioja (UNIR). Doctor en Humanidades y Comunicación por la Univ. de Burgos, con mención de doctorado extraordinario. Licenciado en Química por la Universidad de Valladolid. Docente en la Escuela de Ingeniería y Tecnología y la Escuela de Doctorado de UNIR. Investigador de comunicación (estudio de los medios de comunicación y redes sociales, desinformación y discurso de odio). Reconocido un sexenio de investigación.

Referências

Anker, A. E., Feeley, T. H., McCracken, B. y Lagoe, C. A. (2016). Measuring the effectiveness of mass-mediated health campaigns through meta-analysis. Journal of health communication, 21(4), 439-456. https://doi.org/10.1080/10810730.2015.1095820

Barrie, C. y Chun-ting, H. J. (2021). AcademictwitteR: an R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software, 6(62), 3272. https://doi.org/10.21105/joss.03272

Bradshaw, A. S. (2023). #DoctorsSpeakUp: exploration of hashtag hijacking by anti-vaccine advocates and the influence of scientific counterpublics on Twitter. Health Communication, 38(10), 2167-2177. https://doi.org/10.1080/10410236.2022.2058159

Budenz, A., Klassen, A., Purtle, J., Yom Tov, E., Yudell, M. y 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

Cuesta-Cambra, U., Peñafiel Saiz, C., Terrón Blanco, J.L., Bustamante Ospina, E., Gaspar Herrero, S. (coords.) (2017). Comunicación y salud. Dextra Editorial.

Cuesta-Cambra, U., Martínez-Martínez, L. y Niño-González, J.I. (2019). An analysis of pro-vaccine and anti-vaccine information on social networks and the internet: Visual and emotional patterns. El Profesional de la Información, 28(2), e280217. https://doi.org/10.3145/epi.2019.mar.17

Chen, J. y Wang, Y. (2021). Social Media Use for Health Purposes: Systematic Review. Journal of Medical Internet Research, 23(5), e17917. https://doi.org/10.2196/17917

Díaz-Campo, J., Cambronero-Saiz, B. y Chaparro-Domínguez, M. A. (2023). Use of twitter for health communication: a systematic review. Revista Española de Comunicación en Salud, 14(1), 95-105. https://doi.org/10.20318/recs.2023.7049

DuPont-Reyes, M. J., Zou, W., Li, J., Villatoro, A. P. y Tang, L. (2025). A machine learning language model approach to evaluating mental health awareness content across Spanish-and English-language social media posts on Twitter. Social Psychiatry and Psychiatric Epidemiology, 1-11. https://doi.org/10.1007/s00127-025-02870-y

Edo-Osagie, O., De La Iglesia, B., Lake, I. y Edeghere, O. (2020). A scoping review of the use of Twitter for public health research. Computers in biology and medicine, 122, 103770.

Eurostat (2021). Community survey on ICT usage in households and by individuals. https://ec.europa.eu/eurostat/cache/metadata/en/isoc_i_esms.htm

FECYT (2022). Desinformación científica en España. Informe de resultados. FECYT / IBERIFIER. https://www.fecyt.es/es/publicacion/desinformacion-cientifica-en-espana

Gaysynsky, A., Senft Everson, N., Heley, K. y Chou, W. (2024). Perceptions of health misinformation on social media: Cross-sectional survey study. JMIR Infodemiology, 4, e51127. https://doi.org/10.2196/51127

Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H. y Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. Plos One, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924

Galderisi, S., Heinz, A., Kastrup, M., Beezhold, J. y Sartorius, N. (2015). Toward a new definition of mental health. World psychiatry, 14(2), 231.

Gil-Pascual, J.A. (2021). Minería de Textos con R. Aplicaciones y técnicas estadísticas de apoyo. UNED.

Gohil, S., Vuik, S. y Darzi, A. (2018). Sentiment analysis of health care tweets: review of the methods used. JMIR Public Health and Surveillance, 4(2), e5789. https://doi.org/10.2196/publichealth.5789

Hamilton, J. L., Torous, J., Szlyk, H. S., Biernesser, C., Kruzan, K. P., Jensen, M., Reyes-Portillo, J., Primack, B. A., Zelazny, J. y Weigle, P. (2024). Leveraging Digital Media to Promote Youth Mental Health: Flipping the Script on Social Media-Related Risk. Current treatment options in psychiatry, 11(2), 67–75. https://doi.org/10.1007/s40501-024-00315-y

Hart, M., Stetten, N. E., Islam, S. y Pizarro, K. (2017). Twitter and public health (Part 1): how individual public health professionals use Twitter for professional development. JMIR Public health and surveillance, 3(3), e60. https://doi.org/10.2196/publichealth.6795

Hasan, F., Foster, M. M. y Cho, H. (2023). Normalizing Anxiety on Social Media Increases Self-Diagnosis of Anxiety: The Mediating Effect of Identification (But Not Stigma). Journal of health communication, 28(9), 563–572. https://doi.org/10.1080/10810730.2023.2235563

Hawkins, J. B., Brownstein, J. S., Tuli, G., Runels, T., Broecker, K., Nsoesie, E. O., Mclver, D. J., Rozenblum, R., Wright, A., Bourgeois, F. T. y Greaves, F. (2016). Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Quality & Safety, 25(6), 404-413. https://doi.org/10.1136/bmjqs-2015-004309

Hawkins, D. S. (2023). “When you Search a# Hashtag, it Feels Like You’re Searching for Death:” Black Twitter and Communication About Police Brutality Within the Black Community. Social Media+Society, 9(2), 20563051231179705.

Huertas Ciórraga, E. (2021). Análisis de los materiales de información a la ciudadanía de la campaña del Ministerio de Sanidad “Este virus lo paramos unidos” publicados entre marzo y mayo de 2020. Revista Española De Comunicación En Salud, 12(2), 121-134. https://doi.org/10.20318/recs.2021.5750

Jiang, B. y Wang, D. (2024). Perception of misinformation on social media among Chinese college students. Frontiers in psychology, 15, 1416792. https://doi.org/10.3389/fpsyg.2024.1416792

Kanchan, S. y Gaidhane, A. (2023). Social media role and its impact on public health: A narrative review. Cureus, 15(1), e33737. https://doi.org/10.7759/cureus.33737

Keib, K., Himelboim, I. y Han, J. Y. (2018). Important tweets matter: Predicting retweets in the #BlackLives-Matter talk on Twitter. Computers in Human Behavior, 85, 106-115. https://doi.org/10.1016/j.chb.2018.03.025

Kim, D., Park, C., Kim, E., Han, J. y Song, H. (2022). Social Sharing of Emotion During the COVID-19 Pandemic. Cyberpsychology, behavior and social networking, 25(6), 369–376. https://doi.org/10.1089/cyber.2021.0270

Malik, A., Ayuso-Mateos, J. L., Baranyi, G., Barbui, C., Thornicroft, G., van Ommeren, M. y Akhtar, A. (2023). Mental health at work: WHO guidelines. World Psychiatry. https://doi.org/10.1002/wps.21094

Manwell, L. A., Barbic, S. P., Roberts, K., Durisko, Z., Lee, C., Ware, E. y McKenzie, K. (2015). What is mental health? Evidence towards a new definition from a mixed methods multidisciplinary international survey. BMJ open, 5(6), e007079. https://doi.org/10.1136/bmjopen-2014-007079

Moorhead, S.A., Hazlett, D.E., Harrison, L., Carroll, J.K., Irwin, A. y Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15(4), e85. https://doi.org/10.2196/jmir.1933

Nanath, K. y Joy, G. (2023). Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach. Behaviour & Information Technology, 42(2), 196-214. https://doi.org/10.1080/0144929X.2021.1941259

Naslund, J. A. y Deng, D. (2021). Addressing Mental Health Stigma in Low-Income and Middle-Income Countries: A New Frontier for Digital Mental Health. Ethics, medicine, and public health, 19, 100719. https://doi.org/10.1016/j.jemep.2021.100719

Norman, C.D. y Skinner, H.A. (2006). eHealth Literacy: Essential Skills for Consumer Health in a Networked World. Journal of Medical Internet Research, 8(2), e9. https://doi.org/10.2196/jmir.8.2.e9

Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin Press.

Park, H., Reber, B. H. y Chon, M. G. (2016). Tweeting as health communication: health organizations’ use of Twitter for health promotion and public engagement. Journal of health communication, 21(2), 188-198. https://doi.org/10.1080/10810730.2015.1058435

Pavlova, A. y Berkers, P. (2020). Mental health discourse and social media: Which mechanisms of cultural power drive discourse on Twitter. Social Science & Medicine, 263(2020), 113250. https://doi.org/10.1016/j.socscimed.2020.113250

Rice, R.E. y Atkin, C.K. (Eds.). (2012). Public communication campaigns. SAGE publications.

Robinson, P., Turk, D., Jilka, S. y Cella, M. (2019). Measuring attitudes towards mental health using social media: Investigating stigma and trivialisation. Social Psychiatry and Psychiatric Epidemiology, 54(1), 51–58. https://doi.org/10.1007/s00127-018-1571-5

Soria-Martínez, M., Navarro-Pérez, C.F., Pérez-Ardanaz, B. y Martí-García, C. (2024). Conceptual framework of mental health literacy: Results from a scoping review and a Delphi survey. International journal of mental health nursing, 33(2), 281–296. https://doi.org/10.1111/inm.13249

Suarez-Lledo, V. y Alvarez-Galvez, J. (2021). Prevalence of health misinformation on social media: systematic review. Journal of Medical Internet Research, 23(1), e17187. https://doi.org/10.2196/17187

Sunstein, C.R. (2017). #Republic: Divided Democracy in the Age of Social Media. Princeton University Press.

Swire-Thompson, B. y Lazer, D. (2020). Public health and online misinformation: challenges and recommendations. Annual Review of Public Health, 41(1), 433-451. https://doi.org/10.1146/annurev-publhealth-040119-094127

Teodorowski, P., Rodgers, S. E., Fleming, K. y Frith, L. (2022). Use of the Hashtag# DataSavesLives on Twitter: exploratory and thematic analysis. Journal of Medical Internet Research, 24(11), e38232. https://doi.org/10.2196/38232

Thackeray, R., Neiger, B. L., Smith, A. K. y Van Wagenen, S. B. (2012). Adoption and use of social media among public health departments. BMC public health, 12, 1-6. https://doi.org/10.1186/1471-2458-12-242

Vaagan, R. W., Torkkola, S., Sendra, A., Farré, J. y Lovari, A. (2025). A critical analysis of the digitization of healthcare communication in the EU: A comparison of Italy, Finland, Norway, and Spain. International Journal of Communication, 19. https://ijoc.org/index.php/ijoc/article/view/15399

Ventola, C. L. (2014). Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics, 39(7), 491. https://pmc.ncbi.nlm.nih.gov/articles/PMC4103576/

Walker, K.K. y Burns K. (2019). #13ReasonsWhy health professionals and educators are tweeting: A systematic analysis of uses and perceptions of show content and learning outcomes. Health Communication, 34(10), 1085–94. https://doi.org/10.1080/10410236.2018.1461171

Wang, X. y Lee, E.W. (2020). Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach. Internet Research. 31(2), 401-418. https://doi.org/10.1108/INTR-04-2020-018

Wagner, B., Horn, A.B. y Maercker, A. (2014). Internet-based versus face-to-face cognitive-behavioral intervention for depression: a randomized controlled non-inferiority trial. Journal of affective disorders, 152-154, 113–121. https://doi.org/10.1016/j.jad.2013.06.032

Yoo, W. (2019). How Risk Communication via Facebook and Twitter Shapes Behavioral Intentions: The Case of Fine Dust Pollution in South Korea. Journal of Health Communication, 24, 663-673. https://doi.org/10.1080/10810730.2019.1655607

Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395(10225), 676. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30461-X/

Zhang, Z., Reavley, N., Armstrong, G. y Morgan, A. (2025). Public disclosures of mental health problems on social media and audiences' self-reported anti-stigma effects. Health promotion international, 40(1), daae204. https://doi.org/10.1093/heapro/daae204

Zheng, H., Goh, D. H. L., Lee, E. W. J., Lee, C. S. y Theng, Y. L. (2022). Understanding the effects of message cues on COVID‐19 information sharing on Twitter. Journal of the Association for Information Science and Technology, 73(6), 847-862. https://doi.org/10.1002/asi.24587