Background/Purpose: The ability to actively listen to and interpret the patient experience is vital to effectively address the needs of individuals within a particular community. A proprietary artificial intelligence (AI) analytics engine was used to identify prevalent concepts and terms in social media data using natural language processing (NLP). This AI platform was applied to social media conversations on the topic of gout to hear and report experiences directly from patients and their communities. Though it has been established that there is an association between mental health disorders and gout, the circumstances behind these associations are not yet fully understood; this remains an unmet need in the gout community.

Methods: The AI platform was used to gain insights into the mental health experience of the gout community. This platform leveraged a variety of NLP techniques to identify prevalent terms and concepts in conversations. We evaluated two social media sources: a private Facebook group, The Gout Support Group of America (12,992 members from 99 countries), which contained 8,500 posts/comments gathered in 2021; and a public subreddit (r/gout) that included 100,000 posts/comments from 9,416 members over more than 10 years (2011-2022).

Results: The AI platform identified conversations with a high probability of discussing ‘mental health’ (score >0.90). In all, approximately 4% of statements from these large gout forums related to mental health, of which 38% were related to stress, 22% were related to depression, and 16% were related to anxiety. Next, conversations with a high probability of discussing ‘management’ were extracted to identify prevalent topics for managing gout (score >0.99); roughly 25% of all statements remained. For these “high-management” conversations, both Facebook and Reddit groups revealed topics related to ‘urgent care’ and ‘primary care’. Out of all “high-management” statements, 0.5% (approximately 1/200) mentioned ‘urgent care’, and 0.6% (approximately 1/150) mentioned ‘primary care’. Emotional affect was contrasted between ‘urgent care’ and ‘primary care’ conversations. Results showed that ‘primary care’ statements were more positive, with a positive-to-negative word ratio of 2.5:1 vs 1:2 in ‘urgent care’ statements. Further, the most common emotional affect identified in ‘primary care’ conversations was ‘trust’ (6% of all words), whereas ‘fear’ was the most frequent affect in ‘urgent care’ (11% of all words).

Conclusion: Using an advanced AI system, we characterized types and associated affect of social media conversations made in two large online gout communities. Mental health was mentioned in approximately 4% of all posts/comments, with stress, depression, and anxiety being referenced most frequently. A sharp contrast in comment emotional affect was seen with site of patient care: primary care was associated with “trust” and urgent care was most associated with “fear” in patients with gout. Interrogation of real-world information in gout-related social media conversations can be invaluable to elucidating the intersection of mental health and disease management, which is typically not apparent by traditional approaches.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/real-world-evidence-from-social-media-provides-insights-into-patient-mental-health-outcomes-in-the-management-of-gout/