Social Listening in Gout: Impact of Proactive vs. Reactive Management on Self-Reported Emotional States

The goal of the current study was to compare general sentiment and emotional language associated with reactive vs. proactive management of gout. A proprietary NLP engine and open-source methodologies were applied to self-reported social media posts on gout to directly examine the experiences of patients with gout and those of their communities. This methodology identifies prevalent concepts and terms using NLP [35].

Data were obtained from two social media sources: a private Facebook group, The Gout Support Group of America (13,860 members), which contained 12,986 posts/comments gathered from 2021 to 2022; and a public subreddit, r/gout (9416 members), which included 107,231 submissions/replies over more than 10 years (2011–2022). More than 120,000 total posts, comments, submissions, and replies were included in the analyses.

All social media statements were submitted to a proprietary machine learning engine designed to identify statements discussing disease management as well as the most frequently used terms and overarching concepts (e.g., clinical findings, medications, practitioners, health care settings), based on the number of conversations surrounding each concept. This engine leverages neural classifiers trained on social media posts and comments annotated with whether individual statements (i.e., sentences) referenced disease management. Each statement is assigned a score between 0 and 1, with 1 representing the highest possible probability that a statement discusses management. Conversations with a high probability of discussing management were extracted to identify prevalent topics for gout management. A cutoff score of 0.99 was implemented to ensure that the included statements had a high probability of pertaining to disease management.

Disease management statements were then subject to term-based filtering using the Unified Medical Language System meta-thesaurus [36] to isolate statements related to reactive or proactive management. Statements were identified by filtering for English meta-thesaurus concepts associated with reactive (e.g., “walk-in clinic”, “urgent care”) and proactive (e.g., “primary health care”, “primary care provider”) sites of care and health care professionals.

Sentiment—a measure of how emotionally positive or negative a body of text is—was measured via continuous and discrete outcomes. Continuous sentiment, or polarity, was evaluated for all statements in reactive management and proactive management groups. Polarity refers to how positive or negative language tends to be in any given text (e.g., positive: “I liked the food”; negative: “service was terrible”). Using TextBlob, a Python package used to process and analyze text data [37], all statements were scored from − 1 (most negative) to + 1 (most positive) to evaluate language polarity by management type. TextBlob relies on a lexicon-based approach to tag word-, sentence-, and paragraph-level text with a continuous sentiment score. This approach provides a score based on all words in a sample of text, which provides a general measure of sentiment for the entire sample and reduces the impact of individual words in scoring. Discrete sentiment was assessed to characterize the proportion of positive and negative words, as well as other emotional affects (e.g., “anger”, “anticipation”, “disgust”, “fear”, “joy”, “sadness”, “surprise”, “trust”) by management type. Analyses employed a bag-of-words approach, which involved extracting relevant text features (e.g., open class words: nouns, adjectives, verbs) and removing uninformative text, such as function words (e.g., “the”, “is”), punctuation, and single-letter words. This word-level approach was implemented for a focused analysis on individual words that may be driving conversation by management type. Words were assigned to emotion-based categories using the NRCLex database, a database of more than 27,000 words from the National Research Council Canada and NLTK WordNet Synonyms [38]. A custom filter was then applied to remove word affects that artificially impact medical-based social media text, such as “urgent” in “urgent care” and “care” in “primary care doctor.”

Polarities were compared between management types using a Welch’s two-sample t test with effect size determined by Cohen’s d. Discrete sentiment was compared between management types using a Pearson χ² test. Subsequent comparisons of proportions by group (e.g., emotion categories, substances) used a Pearson χ² test with Yate continuity correction. Post hoc analysis implemented Bonferroni correction for multiple comparisons. All statistical analyses were implemented in R [39]. Statistical significance was defined as P < 0.05.

This study was conducted with an exemption granted under 45 CFR § 46.104(d)(2) by the Western Institutional Review Board. The data in this study come from both publicly (Reddit) and privately (Facebook) accessible sources. TREND Community has established trusted partnerships with specific Facebook groups that have graciously granted TREND Community access to their data, which are essential for the research we are presenting aimed at enhancing public understanding of the subject. This collaborative agreement entails the establishment of formal procedures wherein TREND Community is entrusted with the responsibility of ensuring data stewardship, which includes incorporating privacy safeguards. It is important to note that there was no retention of any data that include private usernames or unique identifiers regarding the private Facebook group. Deidentification measures were employed prior to data analysis. Furthermore, this study does not present any individual patient data that would necessitate consent. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments.

We numerically evaluated the general sentiment of language by management type for all statements in the reactive management and proactive management groups. Figure 1 shows the mean polarities for reactive and proactive management statements. A Welch’s two-sample t test indicated significant differences in polarity [t(995.3) = 11.36, P < 0.001, d =  − 0.69]. Proactive management statements had a significantly more positive mean polarity than did reactive management statements (mean [SD], 0.19 [0.21] vs. 0.03 [0.26], respectively; P < 0.001).

To better understand the language associated with each management type and the emotional affect, we examined the proportions of positive vs. negative words by management type. Figure 2 shows the positive-to-negative word proportions for proactive vs. reactive management. Pearson χ² test revealed significant differences in positive vs. negative word counts between management types (χ² = 33.0, df = 1, P < 0.001). Reactive care statements had a significantly larger proportion of negative words than proactive care statements (59 vs. 44%, respectively).

Figure 3 and Table 1 show word counts and proportions per emotional state category. Pearson χ² test with Yate’s correction for continuity showed significant differences in emotion category counts between management types (χ² = 95.9, df = 7, P < 0.001). Proactive statements were higher in generally positive categories (“trust” and “joy”), whereas reactive statements included higher negative categories (“anger”, “fear”, and “sadness”). Top “trust” words in proactive care included “specialist”, “finally”, and “advise”. Top “fear” words in reactive care included “pain”, “bad”, and “attack”. Top “sadness” words in reactive care included “pain”, “shot”, and “worse”.

We evaluated the most prevalent medications by management type and compared raw counts for the top 5 medication entities in each group. Pearson χ² test indicated significant differences in counts between management types (χ² = 73.3, df = 4, P < 0.001). Medication entities were significantly different between proactive and reactive management statements, with “allopurinol” appearing significantly more often in proactive management statements and “prednisone/steroids” appearing more often in reactive statements (both P < 0.001; Table 2, Fig. 4).

It is important to note that proactive and reactive statements may not be fully mutually exclusive samples. One of the concepts used to capture proactive care conversations included mentions of primary care providers (PCPs). It is possible that some conversations regarding PCPs include medical intervention that more directly aligns with a reactive approach, as some PCPs adhere to a “treat-the-symptom” approach in managing gout. Given the negativity associated with reactive management in the current study, the contrast in sentiment between the proactive and reactive statements could be further exaggerated with entirely distinct samples. Nevertheless, significant differences in sentiment and emotions were revealed between proactive and reactive management types where each sample was characterized by a unique lexical profile. Future work should explore differences in rheumatologist-focused experiences with reactive experiences in gout.

This is one of the first SML studies to explore mental and emotional experiences related to gout management. As such, these novel findings are likely best interpreted within the context of emerging literature on the topic. Although this study evaluated a considerable number of statements to understand community perspectives, how well these findings apply to the general population is not easily ascertained. Facebook and Reddit comprise international membership across a range of racial and ethnic backgrounds; however, it is difficult to appraise the degree to which specific and/or underrepresented populations are represented in the current study. As a strength, using SML allows researchers to understand patient perspectives that are completely voluntary. Moreover, the minimum requirements for participation using SML are a computer and internet access. While this is a substantial barrier for some individuals, this methodology eliminates patient travel requirements and avoids study burden, survey fatigue, and other barriers that typically exist with conventional methods.