Preventive medications for migraine include both non–disease-specific drugs (eg, antihypertensives and anticonvulsants) and, more recently, disease-specific anti–calcitonin gene–related peptide (CGRP) therapies [1]. Randomized controlled trials (RCTs) demonstrate the clinically relevant efficacy and tolerability of anti-CGRP monoclonal antibodies (mAbs) [2]. However, they may not accurately reflect the real-world experiences of more heterogeneous and diverse patient populations who often have a higher disease burden and more comorbidities [3-6]. In addition to direct reports of potential adverse events from health care providers and patients, regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, which should be handled similarly to spontaneous reports with the same requirements applied [7]. While social media represents an obvious venue in this context, its usage for pharmacovigilance is still in its infancy [8]. Complementary methods of postmarketing surveillance for adverse events with social media are needed.

The last 15 years were characterized by a vast proliferation of social media platforms, allowing for both the rapid dissemination of medical information, as well as direct insight into patient perspectives. Indeed, migraine is most prevalent in individuals between 15-49 years of age, who are among the most active users of the internet [9]. This is also reflected by the growth of migraine-related content on the internet [10-12]. The anonymous social media discussion board Reddit has over 430 million monthly active users and serves as a popular forum for health-related discussions [13]. Unlike other digital media platforms, Reddit provides easily accessible and legal ways of accessing historic forum data. Anonymous forums like Reddit allow users to share personal medical experiences and advice without social stigma. Collectively, the migraine-related subforums (or “subreddits”) such as r/Migraine have a user base exceeding 54,100. While these platforms allow for a democratic exploration of patient experiences and perspectives, they are relatively understudied.

Computational linguistics, a discipline encompassing automatic manipulation and quantitative analysis of oral or written language, represents a potential way to explore this content. Computational linguistics can be applied to identify clusters of words in a dataset with the goal of deriving dominant thematic elements. Identifying word distribution frequencies in specific groups of texts is a common technique used in computational linguistics with multiple wide-ranging applications [14]. In this study, we applied computational linguistics, specifically word frequency analysis, to characterize and identify self-reported adverse events associated with the new therapeutic anti-CGRP mAbs reported by users on Reddit. To ascertain the reliability and validity of our methodology, we first conducted a preliminary study with older-generation migraine drugs to see if we could identify known adverse reactions. After validation, we applied the methodology to the newer generation of CGRP therapies for migraine prevention.

In this study, we applied computational linguistics methods to unfiltered discussions about migraine treatments on Reddit to distinguish patient-reported adverse effects associated with these medications. The validation phase of our study accurately identified common adverse events for standard migraine preventive treatments. For example, typical adverse events such as “taste” and “tingling” appeared in posts for topiramate. After applying this methodology to mAbs targeting CGRP or its receptor—fremanezumab and erenumab, respectively—no definite adverse events were flagged for fremanezumab. Notable flagged words for erenumab included “constipation,” “depression,” and “vomiting.”

In our proof-of-concept analysis, we confirmed that keywords discussed in relation to topiramate from the Reddit r/Migraine subforum are consistent with the most common adverse events observed in RCTs [18]. Interestingly, dizziness, a common side effect, did not appear when comparing propranolol to either the control corpus or topiramate [19]. A possible explanation could be that many different words were used to describe this side effect, for example, “lightheadedness” and “dizzy,” which were detected individually but did not meet statistical significance after correcting for multiple comparisons. Because patients may use a variety of different words to describe a single symptom, pooling these terms be useful in future studies when analyzing social media data.

While constipation was not a major concern during RCTs with erenumab, emerging real-world data report that it is among the most frequent reasons for treatment discontinuation [20,21]. Constipation was subsequently added to the US Food and Drug Administration (FDA) label after regulatory approval [22]. During RCTs, approximately 2% of participants reported constipation compared to approximately 10% to 20% of real-world users [3,20,23]. Our findings further support this, since Reddit users frequently discuss constipation and vomiting in relation to erenumab. The discrepancy between data from RCTs and real-world experiences may be due to a more heterogeneous patient population receiving erenumab in the postmarketing phase. This population likely includes patients with other medical comorbidities or factors that could have excluded them from consideration in clinical trials. Increasing evidence suggests that individuals with migraine may have a higher prevalence of comorbid gastrointestinal disorders when compared with the general population [24,25].

Interestingly, the word “muscle” was associated with posts discussing erenumab when compared with fremanezumab. Although we did not include this word as a potential adverse event due to its ambiguity, it could be related to muscle cramps, which were added to the FDA warning label for erenumab after approval [22]. Muscle spasms occurred in approximately 2% of patients in RCTs [20]. Whether “muscle”-related adverse effects are occurring at a more significant rate in real-world use is unclear. Additional investigation into the extent and severity of muscle disease due to erenumab could be considered in future studies or when examining other postmarketing surveillance data sources for verification.

Another notable postmarketing safety update was the addition of hypertension to the FDA label for erenumab; however, this was not flagged in our analysis. In RCTs, less than 1% of participants reported hypertension, but participants with severe cardiovascular risk factors were excluded from these trials, and hypertension has not been commonly noted in other studies [3,23]. It is possible that cardiovascular risk factors are screened to a higher degree than gastrointestinal disorders. Patients at risk for hypertensive complications might, therefore, be more likely to decline treatment with this drug. Finally, worsening of hypertension is typically asymptomatic, and patients may not recognize this as a potential adverse event to raise in social media discussions.

There were no keywords flagged in the fremanezumab dataset. The finding that constipation was flagged for erenumab but not for fremanezumab could be related to pharmacodynamic differences in the mechanism of action. Erenumab targets the CGRP receptor, while fremanezumab targets its ligand. One study offers a possible mechanistic model that strengthens this hypothesis, specifically, via direct effects of CGRP on intestinal motility modulated by other receptors in addition to the canonical CGRP receptor [26]. However, galcanezumab, another mAb targeting the CGRP ligand, also had constipation listed as a common adverse reaction in clinical studies and postmarketing reports [27]. Further data are needed to clarify any potential differences in mechanism.

A strength of our study is the inherent anonymity of the Reddit forum, which allows us to analyze information that patients might not otherwise feel comfortable discussing or prioritize sharing during a clinical consultation. Querying the Reddit migraine subforum also provides insight into the experience of more than 54,000 unique users, which could include diverse sources ranging from patients and family members to experienced health care providers.

However, the use of Reddit data also confers significant limitations. We cannot correlate our findings with individual patient demographics, previous medical history, or other clinically relevant information. All information is self-reported and subjective, which calls into question the accuracy of unverifiable data. Although the number of posts differs between erenumab and fremanezumab due to FDA approval time, we do not expect this to significantly change our result since word distribution should be theoretically constant despite the sample size. Another limitation is the use of multiple different words for the same symptom; for example, when “lightheadedness,” “dizzy,” and “dizziness” collectively describe the same entity. Relevant symptoms described in various ways may be missed during this type of analysis. Pooling multiple similar terms could avoid this problem in future studies. Finally, our method did not analyze comments responding to specific posts; incorporating this into future studies using word frequency methodology could increase the accuracy of our conclusions. Future directions for our line of research include the analysis of other digital platforms to compare with our findings.

Postmarketing pharmacovigilance remains critical, especially when RCTs underdetect adverse events. When compared with health care professionals, direct patient reporting of adverse drug events identifies more potential signals and in greater detail [28]. Our study provides support that monitoring patient reports via social media can be incorporated into postmarketing safety surveillance for preventive migraine medications. While our proposed methodology has its limitations, the data offer a broader postmarketing perspective that complements clinical trial findings and case reports. We hope that consideration by relevant stakeholders will enhance existing tools in the pharmacovigilance armamentarium, enabling quicker and earlier detection of adverse effect signals. However, more work is needed to improve the reliability and validity of social media analysis, which faces the same challenges inherent to big data, including high volume, unstructured capture, and structural regulatory barriers, which all need to be addressed before implementation [29].