Previously submitted to: JMIR Mental Health (no longer under consideration since Oct 28, 2024)
Date Submitted: Oct 25, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Unveiling social media considerations in ADHD treatment: Machine Learning study using X´s posts over 15 years.
ABSTRACT
Background:
This study investigates social media content related to Attention-Deficit/Hyperactivity Disorder (ADHD) treatment by analysing public discourse on X (formerly Twitter) over the past 15 years. It differentiates between user types and focuses on medical and non-medical content related to ADHD medications.
Objective:
The study aims to analyze social media content on X (formerly Twitter) related to ADHD medications from 2006 to 2022, classifying the tweets based on user types and the nature of medical and non-medical discussions. It seeks to provide insights into public perceptions of ADHD medications, particularly stimulant and non-stimulant treatments, and their use, misuse, and side effects. Ultimately, the study aims to help healthcare professionals better understand these online discussions and improve their communication with patients, facilitating more informed treatment decisions.
Methods:
An observational study was conducted analysing 254,952 tweets in Spanish and English about ADHD medications from January 2006 to December 2022. Content analysis combined inductive and deductive approaches to develop a categorisation codebook. BERTWEET and BETO models were used for machine learning classification of English and Spanish tweets, respectively. Descriptive statistical analysis was performed.
Results:
Overall, stimulant medications were posted more frequently and received higher engagement than non-stimulant medications. Methylphenidate, dextroamphetamine, and atomoxetine were the most frequently mentioned medications, especially by patients, who emerged as the most active users among the English tweets. Regarding medical content, tweets in English contained more than twice the number of mentions of inappropriate use compared to those in Spanish. There was a high content of online medication requests and offers in both languages.
Conclusions:
Our study underscores the potential of social media, particularly X, in exploring perceptions of ADHD medications. These insights highlight the need for healthcare professionals to stay informed about these conversations on platforms like X. By understanding patient-led discussions, physicians could more effectively address concerns and misconceptions during consultations, leading to better-informed treatment decisions
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