Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study

Area of Science:

• Pharmacology
• Social Media Analysis
• Public Health

Background:

• Antiepileptics and antidepressants are common co-analgesics for chronic pain, but concerns exist regarding efficacy, side effects, and dependency.
• Increased prescriptions and potential fraud led to reclassification of some antiepileptics.
• Online discussions offer insights into real-world perceptions of these medications.

Purpose of the Study:

• To analyze public opinions, perceptions, beliefs, and attitudes towards co-analgesics using social media data.
• To understand patient and healthcare professional perspectives on co-analgesic use.
• To identify key themes and concerns related to co-analgesic medications.

Main Methods:

• Cross-sectional study analyzing 51,167 public posts from X (formerly Twitter) between January 2019 and December 2020.
• Posts were collected in English and Spanish, with 2,000 manually analyzed using a codebook.
• Machine learning classifiers were used to categorize user types and content.

Main Results:

• Anticonvulsants (78%) and analgesic antidepressants (24%) were the most discussed co-analgesics.
• The majority of posts (67%) were from patients, with only 13% from healthcare professionals.
• Key themes included low medication efficacy (70%), side effects (50%), dispensing challenges (25%), and high costs (15%).

Conclusions:

• Social media provides valuable insights into public perceptions of co-analgesics.
• Findings highlight significant patient concerns regarding efficacy and side effects.
• Public health communications should be developed to raise awareness and optimize the use of these pain medications.