JMIR Publications

ABSTRACT

Background:

Real-world data, such as those contained within social media platforms, can summarize diverse patient experiences to detect treatment-related adverse events.

Objective:

To characterize adverse events related to novel calcitonin gene-related peptide (CGRP) therapeutics on Reddit, a large online social media forum.

Methods:

We examined differences in word frequencies from medication-related posts extracted from the Reddit subforum r/Migraine over a ten-year period (2010-2020) using computational linguistics. In the validation phase, we compared the medications propranolol versus topiramate, as well as propranolol and topiramate each against randomly selected posts. In the application phase, we examined posts discussing the CGRP therapeutics erenumab and fremanezumab to determine frequently discussed adverse events.

Results:

From 22,467 Reddit r/Migraine posts, we extracted 402 propranolol posts, 1423 topiramate posts, 468 erenumab posts, and 73 fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events. Erenumab compared against a random selection of terms identified “constipation” as a recurring key word. Erenumab against fremanezumab identified “constipation,” “depression,” “vomiting,” and “muscle.” No adverse events were identified for fremanezumab.

Conclusions:

Computational linguistics, when applied to social media, can identify potential adverse events of interest for novel therapeutics. Social media data represents a promising avenue for pharmacovigilance, but further work is needed to improve reliability and usability.