Exploring the Lived Experience of Acne in the United States and United Kingdom: Insights from Social Media-Based Analysis
ABSTRACT
Background:
Acne is a chronic skin condition that mainly affects adolescents and young adults but can persist into adulthood. It can have repercussions on physical and mental health, self-esteem, and body image. The growing use of social media for health information and peer support offers an opportunity to explore real-life experiences of acne.
Objective:
To analyze social media messages from users in the United States (US) and United Kingdom (UK) using artificial intelligence to assess the impact of acne on quality of life (QoL), identify discussion topics, and explore unmet needs.
Methods:
Data were extracted from public platforms using a query containing the word “acne”, between January 1 and December 31, 2024. Data cleaning and filtering were done using NLP methods and algorithms. Biterm Topic Modeling identified main discussion topics, and QoL impact was assessed using a deep learning algorithm. Unmet needs were identified using the saturation method.
Results:
A total of 646,809 messages posted by 432,234 users were identified. Main topics included skincare routines and product recommendations (24%), acne scars (21%), and general treatment information (15%). Overall, 52.8% of users expressed at least one QoL impact, most frequently related to signs and symptoms (76.8%), social functioning (65.3%), mental health (46.9%), and cost (27.1%). Of 3200 annotated messages, 582 contained unmet needs, including effective solutions for hormonal acne (19.1%), clarity in identifying acne triggers (14.4%), treatment guidance (11.7%), and psychological support (11.7%).
Conclusions:
This study revealed the important physical, psychological, social, and financial impact of acne on QoL and identified several unmet needs. Given the growing role of social media, these findings highlight opportunities for dermatologists and health professionals to educate and engage with the acne community through digital platforms. Keywords: acne, dermatology, Social Media Listening, Natural Language Processing, Machine Learning, infodemiology
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.