Large Language Modeling–Enabled Analysis of Atrial Fibrillation on Social Media

Abstract

Background

Atrial fibrillation (AF) is the most common arrhythmia worldwide, and patient perceptions significantly influence shared treatment decisions. Artificial intelligence–driven analysis of social media may offer valuable insights into contemporary public attitudes toward AF outside clinical settings.

Methods

This qualitative study used large language modeling and advanced artificial intelligence topic modeling techniques to analyze public perceptions of AF from Reddit discussions between April 2006 and November 2023.

Results

We curated 86 323 AF‐related conversations (18 754 posts, 67 569 comments) across 38 183 unique users by searching terms related to AF. Our topic modeling identified 65 distinct discussion topics organized into 9 thematic groups, with topics including personal experiences with treatments (eg, ablation, rate versus rhythm control), roles of health care providers and community support, AF triggers (diet, illicit substances, supplements, stress, caffeine), and anecdotes highlighting the difficulties of living with AF. Discussions commonly reflected 3 main themes: (1) advantages and limitations of wearable devices for AF monitoring, (2) hesitancy and misconceptions about AF treatment, and (3) patient‐centered challenges following an AF diagnosis.

Conclusions

The artificial intelligence–enabled analysis underscored substantial public discourse around patient experiences with AF detection and management. Leveraging social media data to understand patient perspectives on cardiovascular health may inform patient‐centered resources and future research directions to better support patients living with AF.

Nonstandard Abbreviations and Acronyms

EARLY‐AF

Early Aggressive Invasive Intervention for Atrial Fibrillation

EAST‐AFNET 4

Effects of Early Rhythm Control Therapy in Patients With Atrial Fibrillation

STOP AF First

Cryoballoon Catheter Ablation in Antiarrhythmic Drug Naive Paroxysmal Atrial Fibrillation

Research Perspective.

What Is New?

• We developed a large language model–based pipeline to analyze >86 000 Reddit posts related to atrial fibrillation, identifying discussion topics grouped into overarching themes that reflect real‐world patient experiences.

• Our analysis highlights prominent public discourse around device‐detected atrial fibrillation, treatment hesitation, and misconceptions.

What Question Should Be Addressed Next?

• Future studies should assess how online beliefs influence atrial fibrillation treatment decisions and adherence, whether education informed by social media insights can improve outcomes, and if these findings generalize to older or non–Reddit‐using populations.

Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting millions worldwide and posing a significant public health challenge due to its association with increased risk of stroke, heart failure, and death. ¹ Despite its high prevalence and clinical significance, adherence to guideline‐recommended therapies for AF such as anticoagulation remains suboptimal, contributing to preventable morbidity and death. ^{2 , 3}

Patient perceptions and experiences outside of the health care system can strongly affect uptake of and adherence to guideline‐recommended AF therapies. ^{4 , 5} However, patient perceptions around AF diagnosis and management have not been well‐described outside of health care settings. ⁶ Attempts to characterize these factors have included study‐based surveys, focus groups, or interviews, ⁷ but these approaches are subject to observational and selection biases, are expensive to conduct, and often difficult to generalize beyond the clinical setting. ⁸ Reddit users are often younger, more digitally literate, and potentially more engaged in online health dialogue, which can complement traditional survey‐based research approaches.

Social media platforms like Reddit provide a forum for anonymized public discourse on health topics and may identify real‐world experiences not captured in clinical settings or randomized trials. ^{9 , 10} Manual analysis of large volumes of social media content to identify relevant topics of discussion is resource and time‐intensive but may be accelerated using techniques in natural language processing, especially large language models. ¹¹

In this study, we aim to characterize public perceptions about AF from social media using a novel artificial intelligence (AI)‐based pipeline to leverage available patient‐generated data outside the health care system.

Methods

Data Set

Reddit is a popular social media platform that is composed of communities called “subreddits,” which are prefixed by “r/” and are focused on specific topics (eg, r/AskDoctors, r/WorldNews). Subreddits contain discussions composed of threads (“posts”) and responses (“comments”). Most subreddits, including all posts and comments contained within them, are openly visible to the public without the need for a Reddit user account. We curate AF‐related discussions via an application programming Interface called PullPush that indexes and permits retrieval of all openly available Reddit content by searching for discussions containing the following terms: atrial fibrillation, afib, and atrial flutter. These terms were chosen on the basis of prior literature evaluating online materials relating to AF. ^{12 , 13 , 14} Trends around key AF‐related concepts, such as use of the Apple Watch, anticoagulation, and ablation, were extracted by searching for the presence of a set of keywords pertaining to that concept within the body of all discussions (Table S1). The Stanford University Institutional Review Board deemed this study exempt from ethical review and the requirement for informed consent because no human participants were involved. The data for this study can be freely accessed at https://github.com/sssomani/afib_reddit.

Topic Modeling

To understand public perceptions from a large social media data set, we use topic modeling, a natural language processing approach that clusters text bodies and identifies themes associated with each cluster. As reported previously, our topic modeling pipeline starts by first embedding discussions into a numerical representation using a pretrained, document‐level Bidirectional Encoder Representations From Transformers–like architecture model called Beijing Academy of Artificial Intelligence Generalized Embeddings (bge‐base‐en‐version 1.5), which is trained over an extensive text corpus on both supervised and unsupervised techniques to achieve state‐of‐the‐art performance on the Massive Text Embedding Benchmark. ¹⁵ These embeddings were then simplified into a lower‐dimensional representation using the Uniform Mapping Approximation and Projection algorithm to improve clustering performance (Table S2). We first initialized both the number and centroid of clusters using a density‐based clustering algorithm called Hierarchical Density Based Spatial Clustering of Applications With Noise and fine‐tuned the assignment of discussions into the appropriate topics using KMeans clustering.

Each topic was labeled using Llama2 (7B), a freely available large language model by Meta, whose family of models previously achieved state‐of‐the‐art performance compared with other open‐source large language models in multiple domains. ¹⁵ We engineered prompts to generate topic labels by passing representative discussions (chosen on the basis of the Euclidean distance to the assigned topic’s centroid), randomly sampled discussions from the topic, and an initial set of topic keywords generated using a Bag‐of‐Words representation (Figure 1). Since topics may be intrinsically distinguished by other embedded features from the model (eg, linguistic style, tone) rather than meaningful content, we clustered the cumulative term‐frequency inverse‐document frequency representation of these topics using spectral clustering to find overarching themes of discussion (“groups”). The number of groups was automatically determined on the basis of optimizing the Silhouette coefficient, which is a mathematical measure of how similar discussions are within a cluster relative to how similar those discussions are to those in other clusters. Group labels were generated by providing prompts to Llama2 (7B) with relevant topic labels (Figure 1). Further details on topic modeling are discussed in Data S1.

Figure 1. AI topic modeling pipeline.

Potential AF‐related discussions are extracted from Reddit using predefined keywords and preprocessed for AI model analysis (“Dataset Curation,” red box). Each discussion is then embedded, dimensionally reduced, and clustered using KMeans to identify key topics (“Topic Identification,” yellow box). Groups are subsequently determined by clustering each topic’s cumulative term‐frequency inverse‐document frequency representation (“Group Identification,” blue box). Topic and group labels are assigned using the large language model Llama, following a specific prompt template (“Topic and Group Labeling With Llama,” purple box). The topic labeling prompt is shown in yellow at the center, while the group labeling prompt appears in blue on the right. AF indicates atrial fibrillation and AI, artificial intelligence.

Sentiment Analysis

A separate Bidirectional Encoder Representations From Transformers–like model, Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach, pretrained on characterizing sentiments from social media posts, was used to classify sentiment. ⁴ The output consisted of 3 probabilities (continuous values between 0 and 1) assigning the likelihood that the input text would have a negative, neutral, or positive sentiment. Sentiment value (“positive,” “negative,” or “neutral”) for that phrase was assigned by choosing the sentiment with the highest probability. Further details around sentiment analysis are discussed in Data S1.

Results

A total of 86 323 discussions, including 18 754 posts and 67 569 comments, were obtained between April 6, 2006, and November 20, 2023. These posts came from 38 183 unique authors, of which 26 716 authored only 1 post. The number of discussions grew an average of 86.1% each year over time (Figure 2A). These discussions took place on 6312 unique subreddits, the most common of which were r/AFIB (10 686 [12.3%] discussions), r/ATRIALFIBRILLATION (8030 [9.3%] discussions), and r/AskDocs (3143 [3.6%] discussions) (Table S3).

Figure 2. AF‐related posts and topics over time.

The number of AF‐related discussions in the data set increased as a fraction of total discussions over time (A) and the fraction of discussions during that year for 3 key terms (Apple Watch, ablation, and anticoagulation) changed over time (B). AF indicates atrial fibrillation.

The absolute (total number of discussions that year) and relative (as a proportion of all discussions that year) change in key AF‐related concepts are represented in Figure 2B. The total number of discussions surrounding anticoagulation was high preceding 2015, with a major spike in that year but has since proportionally decreased. Discussions around ablation have continued to increase over time, although more consistently since 2019. Discussions around the Apple Watch were minimal until 2018 to 2019, when they rose suddenly and have continued to grow slowly since then.

A total of 65 topics of AF‐related discussions were identified (Table S4). The 3 most common topics were around smartwatch detection of AF (topic 1), medical management of AF (topic 2), and ablation for AF (topic 3). Other topics included the link between COVID and AF (topic 5), celebrities and athletes with AF (topic 44), hormonal therapy association with AF (topic 54), experiences around cardioversion (topics 11, 16, and 28), comorbid associations with AF (topics 9, 14, 33, 37, and 50), and AF following supplement use (topics 20, 26, 40, and 48).

Next, a total of 9 overarching groups from these 65 topics were automatically identified (Table1; Figure 3), with a performance Silhouette coefficient of 0.857 (Figure S1). The largest groups included AF and associated triggers and conditions (group 1), AF detected on wearable devices (group 8), discussions around anticoagulation and general AF management, including ablations (groups 2 and 5), and peer‐to‐peer evaluation of providers’ experiences with AF (group 5). Other groups included drug advertisements (groups 3 and 9), anecdotal experiences with AF (group 6), and endocrinological associations with AF (group 7).

Table 1.

Large Language Modeling–Identified Topics and Overarching Groups

Group	Group name	Topics	Topic names
1	Atrial Fibrillation and Related Conditions	4, 6, 8, 9, 13, 16, 19, 21, 25, 28, 32, 36, 39, 40, 43, 47, 51	Vaccine Safety and Efficacy; COVID‐19 Vaccine Research and Data; Personal Experiences With Vaccines and Health Outcomes Heart Palpitations; Afib Detected; Anxiety and Heart Issues Alcohol‐Triggered Afib; Heart Risks From Drinking; Sober Living and Afib Management Afib–Anxiety Overlap; Heart Rhythm Anxiety; Afib Panic Sleep Apnea Diagnosis; Afib Triggered by Sleep; CPAP Treatment for Heart Issues Pain; Afib; Cardiac Afib and Electrolytes: Nutrition and Supplements for Heart Health; Managing Afib With Balanced Diet and Electrolyte Supplements; Electrolyte Imbalance and Afib: How Nutrition Can Help Patient Stories—Atrial Fibrillation; Healthcare Concerns—Insurance and Access; Personal Experience—Midlevel Providers Marijuana and Heart Health: A Complex Relationship; Weed Worsens Afib: Personal Stories and Medical Insights; Heart Condition and Marijuana Use: Navigating the Risks and Benefits Afib + WPW Combo; Heart Rate Anxiety; Epithelial Cell Ablation Gastro‐Cardiac AF; Hiatal Hernia and Atrial Fibrillation; Cold Weather Triggers AF Autoimmune Disorders; Neurological Conditions; Respiratory and Gastrointestinal Issues Caffeine and Afib; Energy Drinks and Heart Rhythms; Decaf vs Regular Coffee for Heart Health Weight Loss and Afib Management; Heart Health and Weight Loss; Afib Prevention and Weight Management Endurance Exercise and Risk of Atrial Fibrillation; Managing Atrial Fibrillation in Athletes; The Impact of Extreme Physical Activity on Cardiac Health Omega‐3 Risk Label: High Doses of Marine Omega‐3 Supplements Aassociated With Increased Risk of Atrial Fibrillation; Keto Safety Label: No Evidence of Increased Risk of Atrial Fibrillation on Ketogenic Diets, Possibly Due to Shift in Gut Microbiome and Metabolome; Nutrient Balance Label: Adequate Intake of Essential Nutrients, Including Protein, B12, and DHA/EPA, Through a Balanced Diet Can Help Mitigate Risk of Atrial Fibrillation Novel Information. Informational Needs. Heart Rate
2	Atrial Fibrillation (Afib) and Related Conditions	5, 15, 20, 23, 30, 37, 38, 42, 45, 46, 54, 58, 60, 63	Afib Blood Thinning; Anticoagulation for Afib; Stroke Risk Management Afib Episodes; Cardioversion Experience; Long‐Term Afib Management Afib; Irregular Heartbeat; Cardiology Check Afib Diagnosis; Managing Afib Symptoms; Treatment Options for Atrial Fibrillation Cardiac Afib; Heart Country; Atrial Fibrillation Atrial Fibrillation (Afib); Irregular Heart Rhythm; Stroke Risk Due to AFib Young Afib; Diagnosed Afib; Atrial Fibrillation Atrial Fibrillation (AF); Cardioembolic Stroke Risk; Home‐Based Education for AF Patients Atrial Fibrillation (Afib); Blood Clots and Stroke Risk; Cardiac Care and Management Afib vs Other Conditions; Diagnosing Afib: Key Documents and Queries; Exploring Afib: Deleted Posts and User Concerns Earnings Release; Upcoming Events; Stock Chart Atrial Fibrillation Market Growth; Cardiac Arrhythmia Monitoring Devices; Atrial Fibrillation Treatment Options Atrial Fibrillation Study; Participate in Medical Research; High Paying Medical Studies Cheney Shoots Whittington; Whittington Injured in Cheney Shooting; Dick Cheney Hunting Incident
3	Cardiovascular Medications and Treatments	44, 50, 52, 53, 59, 61, 64	Hyzaar—Generic High Blood Pressure Medication; Losartan‐Hydrochlorothiazide | Hyzaar | Lower Blood Pressure Risk; Hyzaar | Losartan‐Hydrochlorothiazide | Effective High Blood Pressure Treatment Buy Inderal Online; Generic InnoPran for Sale; Order Propranolol Online Calan (Verapamil): Treats Supraventricular Tachycardia and Heart Rhythm Disturbances. Verelan (Verapamil): Controls Heart Rate Response to Other Rhythm Disturbances; Buy Calan Online: Generic Verapamil for Supraventricular Tachycardia and More Exelon–Constellation Merger Approved. New Energy Giant Formed: Exelon–Constellation Merger Completed; Unlocking Value: Exelon–Constellation Merger Brings Benefits to Shareholders Tiamate—Generic Diltiazem HCl Tablets. Cartia—Generic Diltiazem Tablets; Tiamate—Diltiazem HCl OTC Buy Aspirin—Dipyridamole Online. Generic Aggrenox for Stroke Risk Reduction. Fast Delivery of Aspirin—Dipyridamole Cheap Generic Preven. MD Approved Levonorgestrel; Order Cheap Enpresse Online
4	Cardiac Arrhythmia Management	2, 7, 17, 27, 35, 41	Ablation; Afib; Post‐Ablation Afib Episode Management; Beta Blocker Therapy; Arrhythmia Medication Options Fetterman’s Afib Journey; From Stroke to Recovery; Atrial Fibrillation and Its Risks Afib; Cardioversion; Electrophysiologist Atrial Flutter and Afib Ablation; Electrical Heart Rhythm Issues; Cardiac Arrhythmia Management Opioid Management; Stimulant Therapy; Cardiac Condition Monitoring
5	Cardiovascular Management	1, 10, 11, 24, 31, 34	ICU Patient Management; Afib RVR Management; Cardiac Rhythm Interpretation Afib RVR; Cardioversion With Medications; Rhythm Control Afib With Aberrancy; Irregular Wide Complex Rhythm; Atrial Fibrillation Atrial Tachycardia; Flutter Conduction; Atypical Atrial Activity Cardiac Arrest; Defibrillation; Ventricular Fibrillation Atrial Tachycardia; WPW Syndrome; Supraventricular Tachycardia
6	Health and Wellness	26, 29, 33	Health Care Issues; Family Dynamics; Pet Health Concerns Hospice Care; Chronic Illnesses; Marital Issues Atrial Fibrillation; Heart Disease; Cardiac
7	Endocrine and Cardiac Disorders	49, 55, 62	Hypothyroidism and Afib; Thyroid Storm and TSH Levels; Hyperthyroidism and Cardiac Risks Cardiac Issues and Testosterone; Afib and HRT. TRT and Heart Palpitations
8	Cardiac Monitoring and Afib Detection	0, 3, 12, 14, 18, 22	Apple Watch Afib Detection; Smartwatch ECG Monitoring; Atrial Fibrillation Tracking Afib Detection; Apple Watch ECG Accuracy; Heart Monitoring Atrial Flutter vs PVCs; Managing Afib and PVCs; Fluttering Hearts and Anxiety Irregular Heartbeat; Atrial Fibrillation; Rapid Ventricular Response Afib Looks; Sinus Rhythm Irregularities; Atrial Fibrillation Suspected Possible Afib; Afib Detected; Heart Rhythm Concerns
9	Cardiovascular Medications	48, 56, 57	Generic Nebivolol Online Pharmacy Overnight Delivery; Buy Cheap Generic Bystolic; Where to Order Generic Nebivolol Fast Shipping Where to Buy Advair Diskus Online; Advair Diskus Generic Price in Quebec; Over‐the‐Counter Advair Diskus Retail Price in US Buy Cordarone Online; Generic Nexterone for Sale; Pacerone Without Prescription

CPAP indicates continuous positive airway pressure; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; HRT, hormone replacement therapy; ICU, intensive care unit; OTC, over‐the‐counter; PVC, premature ventricular contraction; RVR, rapid ventricular response; TRT, testosterone replacement therapy; TSH, thyroid‐stimulating hormone; and WPW, Wolff–Parkinson–White.

Figure 3. Topic modeling results.

Scatter plot displaying a 2‐dimensional projection of all discussion embeddings. The overlying color represents the associated group of that discussion based on the topic modeling. The x and y axes represent the 2 axes (feature 1, feature 2) onto which embeddings were dimensionally reduced using Uniform Manifold Approximation and Projection for visualization purposes.

Finally, using a separate, pretrained language model to classify the sentiment of each discussion, 33 499 had negative (38.8%), 46 142 had neutral (53.3%), and 6682 had positive (7.7%) sentiment. Examples of discussions about medications with a negative sentiment included the following: “… I don’t trust the cardiologist I’ve been seeing… he wants to put me on blood thinners”; “Really don’t want to end up on blood thinners!”; and “I was given propranolol and metoprolol (because that’s what they give for anxiety around here); both of those cause afib.” Examples of discussions with a neutral sentiment included “… but I went back into sinus during the night after taking a beta blocker.” Example of discussions with a positive sentiment included “he finally put me on verapamil, a calcium‐channel blocker that works and has few side effects. … I’m now a very happy camper” and “on metoprolol and flecainide as needed when I’m in afib. So far, so good.” The discussions reveal varying patient understanding of the effects of both rate or rhythm control agents and anticoagulants. Procedural rhythm control strategies were represented by discussions such as “After my ablation, all the pills I eat go away and a 95%+ chance my afib will too.” In general, discussions involving rate or rhythm control strategies were associated with more positive sentiment than those mentioning anticoagulation therapies.

Discussion

In this qualitative study, we provide insight into nearly 2 decades of public discourse around AF using a large social media platform. Online AF discussions have grown exponentially over time. We developed a large language model–based topic modeling pipeline that identified 65 topics and 9 representative thematic groups that provided insight into wearable device use advantages and disadvantages, AF management hesitancy and misconceptions, and the patient‐centered difficulties of living with an AF diagnosis, offering a unique lens into unfiltered patient realities not typically captured through traditional research methods. Given the insights afforded by this AI‐enabled topic modeling pipeline to analyze discussions on Reddit, the approach can continue to be leveraged beyond existing therapeutic areas including statins, coronary artery calcium, and glucagon‐like peptide‐1 receptor agonists. ^{4 , 16}

Our analysis reveals greater online discourse about AF, with an average of 80% more discussions each year. Although a large part of this growth is driven by increased uptake of Reddit, rather than a growing interest in AF within the population, the higher volume of content does create a larger repertoire of available online AF‐related perspectives. The proportional increases in online discussions about AF diagnostics and management also parallel key developments in the AF space. For example, the Apple Watch Study results were released in 2019, which align with the increase in discussions about the Applewatch. ¹⁷ Discussions around AF ablations have also proportionally increased and may reflect the growing guidance for earlier consideration of ablation in the management of AF as a rhythm control strategy. ¹⁸ The release of practice‐changing clinical trial results and updated practice guidelines during our study period (2006 and 2023) have likely impacted shared decision making and resulted in the growing public discourse on the topics.

The impact of wearable consumer rhythm monitors and device‐generated associated insights remains poorly studied. Our study shows strong consumer engagement in using the device reports and alerts to discuss a potential diagnosis with their physicians. Given recent advances in AI interpretation of even single‐lead ECGs, patients with previously undiagnosed subclinical AF are being successfully diagnosed earlier and at higher rates. ^{19 , 20} Various trials, including EAST‐AFNET 4 (Effects of Early Rhythm Control Therapy in Patients With Atrial Fibrillation), EARLY‐AF (Early Aggressive Invasive Intervention for Atrial Fibrillation), and STOP AF First (Cryoballoon Catheter Ablation in Antiarrhythmic Drug Naive Paroxysmal Atrial Fibrillation), have quantified the improvement in outcomes associated with early initiation of either pharmacological or ablative interventions. ^{21 , 22 , 23} However, there were multiple representative discussions showcasing consumer anxiety from the large burden of AF notifications, mostly in those already diagnosed with the arrhythmia. Recent work has discovered that patients have measurable declines in self‐perceived health and management of AF with high rates of false‐positive alerts from their device. ²⁴ How often wearable devices alert people, and whether those alerts are accurate, may be an important area for future research, especially in understanding how patients and clinicians respond to such notifications. Improvements in AF diagnostic accuracy are expected; however, arrhythmia alarm fatigue remains a noteworthy consideration before recommendations can be made for widespread public wearable device use for arrhythmia diagnosis and tracking. ²⁵ These findings suggest an opportunity to guide patients on the appropriate use and limitations of consumer wearables during clinic visits, in a similar manner to home blood pressure monitoring. ²⁶ Providers and patients may also benefit from incorporating anticipatory guidance on how to interpret alerts and manage anxiety from frequent notifications.

Our topic model analysis revealed several novel insights into potential reasons in which patients may fail to adhere to prescribed medications to manage AF including anticoagulation and antiarrhythmics. Among the various discussions of medication nonadherence, one recurrent theme in our analysis was a lack of understanding about medication purpose or side effects. These observations are hypothesis generating and warrant further study in more representative cohorts. Of note, comparatively positive perception of rate or rhythm control to anticoagulation parallels current studies examining difference in adherence rates in the 2 drug classes. ^{2 , 6 , 27} These results highlight a need to incorporate educational resources and efforts to improve shared decision making and adherence around commonly used therapies to reduce AF complications. ²⁸ Health systems, professional societies, and AF‐specific support organizations such as STOPAfib.org could use these findings to build targeted educational campaigns that directly address the most common patient misconceptions. ²⁹ In the clinical setting, physicians may preemptively introduce these concerns during shared decision making by framing them as frequently encountered online perspectives. Our findings provide a basis for future confirmatory studies that seek to better quantify and address this care gap.

Addressing misinformation around AF extends beyond medications. In our study, participants discussed the notion that AF ablation “cured” their disease and cited success rates approaching 100%. While notable improvements in AF localization and ablation have been made in the modern era, ablation success rates range from 50% to 80%, with an estimated 2% risk of recurrence yearly. ^{30 , 31} Such public perception of the procedure may partly stem from health care–sponsored sources. A study examining descriptions of AF on >450 US hospitals registered with Medicare found that only 5% of web pages addressed ablation success rate directly, and only 9% mentioned that a potential second ablation may be necessary to achieve sinus rhythm. ³² In addition to clear and descriptive physician‐level encounters with patients, the discrepancy between literature and public‐facing resources should be rectified to provide accurate, data‐driven information.

Finally, improved diagnostic methods and recent advances in AF management have increased patient survival and introduce a need to identify the challenges associated with long‐term AF diagnosis. ³³ Numerous mentions of the stresses of the diagnosis were described in our study, including difficulty returning to normal functional status given fear of AF recurrence, feelings of isolation from friends and family, and increased incidence of depressive episodes. The benefit of emphasizing psychological health extends beyond the mental well‐being of the patient. Studies have suggested a bidirectional relationship between mental health and AF; patients with increased depressive symptoms or negative emotions have increased incidence of symptomatic AF or recurrence after cardioversion. ^{34 , 35} Implementation of mental and social well‐being assessments and support resources in clinical pathways for individuals diagnosed with AF should be investigated moving forward. For those that flag as high risk, these assessments could be paired with referral pathways to patient communities or peer support programs at the time of diagnosis.

Importantly, our findings are also sensitive to the choice of clustering algorithm. While we observed generally high concordance in topic structure across alternative approaches, key themes and groupings can vary on the basis of model assumptions. This highlights the exploratory nature of our pipeline and the importance of triangulating such findings with complementary methodologies in future studies.

Limitations

There are several limitations to consider when interpreting the study results. The anonymous nature of Reddit restricts information about patient demographics such as age, sex, or ethnicity. Previously, usage rates among the age group 18 to 29 years have been >20%, exceeding other subsets and suggesting a potential responder bias on the forum. ³⁶ Additionally, Reddit is 1 social media platform, and our study did not examine data from other sources such as Meta, Instagram, or X. However, the relevance of understanding Reddit discourse has grown with recent industry partnerships. The 2024 OpenAI–Reddit partnership allows real‐time Reddit data to be incorporated into AI models such as ChatGPT, expanding Reddit’s impact on health information accessed by consumers to those who may not use Reddit themselves. ³⁷ This growing footprint highlights the importance of understanding what health‐related content appears on Reddit to both capture unfiltered patient experiences and identify the information patients may be indirectly consuming via AI or search engine summaries. ³⁸ While the motivation of this pipeline is to offer broad themes around AF‐related discussions on Reddit, this may compromise the homogeneity of discussions within each topic. For example, topics may inherently capture linguistic variation rather than content differences that are more relevant to our pipeline, which warrants deeper dives into the discussions within topics for future studies focusing on certain AF‐related aspects. The volume and variability of these data also lead to inherent sensitivity of the resulting topics and groups on the basis of the choice of clustering algorithm, which must be kept in mind when interpreting these results and when considering modifying the clustering algorithm. Finally, spelling errors could lead to potential mislabeling of discussions or exclusion of data that would otherwise be included in the large language model pipeline. Simplification or abbreviation of posts and comments are common on social media platforms, and current inclusion criteria do not account for such variance.

The study data set has important limitations. While Reddit offers a unique lens into patient perspectives outside of the health care setting, its user base is known to skew younger and more technologically inclined people, which may not reflect the typically older AF population. The subreddit culture, including upvote dynamics, moderation policies, and norms favoring charged discussions, may distort the nature of shared content and amplify atypical experiences. Additionally, some of the analyzed subreddits (eg, r/conspiracy) are not health focused and may contain content less relevant or reliable. Social media data can serve as a valuable complement to traditional research data sets by providing real‐time insights and diverse perspectives that may not be captured through conventional methods.

Conclusions

In this study, we curate a database of >86 000 AF‐related social media discussions and use an AI pipeline to characterize public perceptions about AF. Key topics of discussion included wearable device AF detection, patient experiences about AF diagnoses and management, and the patient‐centered challenges associated with an AF diagnosis. Understanding these publicly expressed perspectives informs clinician–patient shared decision making around AF diagnosis and management guides targeted allocation of public health resources and supports the implementation of evidence‐based clinical pathways to ultimately improve AF outcomes.

Sources of Funding

Dr Rodriguez was funded by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (R01HL168188; R01HL167974; R01HL169345) and the Doris Duke Foundation (Grant No. 2022051). Dr Shah reports consulting fees from ARTIS Ventures, Broadview Ventures, and Bristol Meyers Squibb. Dr Rogers was funded by grants from the National Institutes of Health (K23HL166977) and the American Heart Association (23CDA933663).

Disclosures

Dr Rodriguez reports consulting fees from Novartis, Novo Nordisk, Movano Health, Kento Health, Inclusive Health, Edwards, Arrowhead Pharmaceuticals, HeartFlow, iRhythm, Amgen, and Cleerly Health outside the submitted work. The remaining authors report no relevant disclosures or competing interests.

Supporting information

Data S1

Tables S1–S4

Figure S1

References 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53