This study aimed to systematically review and analyze the research progress and thematic evolution of SMM in the context of health by constructing a fully automated and reproducible bibliometric analysis framework. To achieve this objective, we designed a multistage methodological pathway in accordance with BIBLIO (Preliminary Guideline for Reporting Bibliometric Reviews of the Biomedical Literature) [34]. Figure S1 in Multimedia Appendix 1 illustrates the research process. The methods are elaborated in more detail in Figure S2 in Multimedia Appendix 1. Additionally, the BIBLIO checklist has been provided in Checklist 1.

The first phase of this study involved constructing a comprehensive literature dataset. We selected the PubMed database as the primary data source, as it is one of the most extensive and authoritative bibliographic databases in the field of health research. We summarized the search strategy, record identification, and inclusion process using an adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram. Following the PRISMA-S extension for reporting literature searches, we have presented the search process in Checklist 2 to strengthen the transparency and reproducibility of the search [35]. To ensure the comprehensiveness of the search, we used a search strategy utilizing Boolean logic operators to combine free-text terms and Medical Subject Headings (MeSH) terms. The query included terms for social media platforms (eg, “social media,” “Twitter,” and “Reddit”), combined with terms related to data mining and analysis (eg, “mining” and “data mining”). We also applied filters to include only journal articles from January 01, 2015, to July 31, 2025 (excluding news and retracted articles). The literature download process was automated using the Entrez module. Necessary parameters, including Entrez.email and Entrez.api_key, were configured to comply with National Institutes of Health application programming interface (API) access guidelines and prevent connection errors during high-frequency requests. All retrieved results were stored to ensure transparency and traceability throughout the research process. Furthermore, to mitigate data loss due to API updates or network instability, we implemented comprehensive logging after each batch download and recorded completion timestamps, the search strategy used, the number of records returned, and other relevant metadata, thereby guaranteeing data integrity and reproducibility.

In addition to the metadata obtained from PubMed, this study integrated supplementary metrics from external databases and official journal websites. Specifically, we collected the H-index of relevant journals from the SCImago database to reflect their academic prestige and long-term impact. Concurrently, the most recently released impact factor (IF) and 5-year IF were acquired from official journal websites and matched with the publication records. Then, we retrieved the relative citation ratio (RCR) of the included articles, which is a normalized measure of citation influence across fields and time. The RCR was used only for post-hoc external comparison and was not included in embedding, clustering, parameter selection, or labeling. The integration of these supplementary data laid a solid foundation for subsequent external validity checks.

During data processing, we extracted the core metadata, including title, keywords, abstract, publication year, journal, etc. Then, we performed keyword standardization by unifying plurals and synonyms, expanding abbreviations, and mapping entry terms to MeSH descriptors with the NLM MeSH descriptor dataset (accessed on July 31, 2025; latest record revision date: July 15, 2025).

After processing the bibliographic dataset, we conducted an exploratory descriptive analysis to characterize SMM in health research. This stage provided a phenomenon-level overview to guide subsequent structural modeling. We explored annual publication trends with autoregressive integrated moving average (ARIMA)-based exploratory projection, collaboration networks between institutions and researchers, countries, citation impact, and keyword dynamics (keyword statistics and Kleinberg burst detection). Overall, this multifaceted descriptive analysis established an overview of the research. The methods of keyword burst detection and parameter robustness assessment are described in Multimedia Appendix 2.

This study proposes an automated bibliometric pipeline specifically designed for literature on SMM in the health domain. Although we performed normalization, residual terminological variations may still exist. Therefore, we developed an automated process integrating structural and semantic similarity, where semantic proximity is derived from contextual embeddings (PubMedBERT and SPECTER2) computed from titles and abstracts to help reduce fragmentation caused by near-synonymous or variably expressed terms and to capture contextual semantics. A hybrid similarity matrix was reduced with Uniform Manifold Approximation and Projection (UMAP) and clustered via Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) under multiple parameter profiles. Outputs included interpretable topic clusters with representative keywords and their positions in a 3D strategic diagram (maturity, influence, and recency). In this study, the number of papers published for a keyword represents maturity, the number of connections a keyword has in co-occurrence networks represents influence, and the proportion of papers published within the last 5 years represents recency. Details of parameter tuning, thresholds, and ablation tests are provided in Multimedia Appendix 3. After clustering, we performed an intercluster relationship analysis on the weighted keyword co-occurrence network to quantify cross-theme connectivity. We computed a cluster-to-cluster coupling metric based on the total cross-cluster co-occurrence strength, ranked cluster pairs to identify the most strongly connected themes, and interpreted these links (information is provided in the Results section). To explain what drives these cross-cluster connections, we further identified pair-specific bridging keywords contributing the most to the coupling between each highly coupled cluster pair.

For the dynamic analysis, the study period was divided into 3 slices with the cutoff of explosive growth: 2015‐2019, 2020‐2023, and 2024‐2025. Within each slice, we performed spectral clustering on the keyword co-occurrence network. This allowed us to observe the emergence, growth, and decline of research themes over time. Details of the process are provided in Multimedia Appendix 3.

This study conducted either macro-level or micro-level validation. To assess the robustness of our methods, we conducted internal validation through a series of sensitivity analyses. Moreover, we validated the findings with external checks using RCR alignment.

In addition, we conducted micro-level interpretive triangulation through evidence mapping and expert content analysis. The 3D strategic diagram was divided into 8 quadrants based on a reference point (x=0, y=3.51, z=0.5). From each cluster in each quadrant, we sampled 1 representative keyword that was the farthest from the reference point. For each sampled keyword, we retrieved 2 full-text articles: 1 with the highest RCR and 1 published in a journal with the highest IF. Two reviewers examined the full texts to extract evidence on research introduction, methods, social media sites, contributions, challenges, and future outlook. The protocols for the analysis of the selected articles are described in Multimedia Appendix 4. This interpretive triangulation was performed after the mathematical analyses were finalized and was not used to fine-tune parameters, select models, modify cluster assignments, or remove records and keywords.

All analyses were conducted primarily in Python (via PyCharm IDE), with the exception of burst detection, which was implemented in RStudio (Posit). Microsoft Excel was used for supplementary manual inspection and verification of selected outputs, and Microsoft Office Visio and Draw.io were used to draw flow charts.

This study was a bibliometric and secondary analysis of bibliographic data about academic publications from an open-access database. Neither humans nor animals were involved in this work. Therefore, ethical approval was not required. Moreover, the original data for this study consisted entirely of publicly available bibliographic information. During the research process, we did not collect any individual-level participant data or recruit human participants. Therefore, informed consent was not required, and no compensation was provided. Furthermore, the study exclusively processed publicly available article-level metadata and reported the results in summary form. None of the data collected, processed, or included in the manuscript text and supplementary materials contain any personally identifiable information. Consequently, there is no risk of individual privacy disclosure, and no additional authorization or consent documents are required.

In the initial stage, we retrieved 250 publications from PubMed covering the period between January 1, 2015, and July 31, 2025. After the exclusion of articles without an abstract or author-supplied keywords, a total of 189 publications were retained for systematic analysis (Multimedia Appendix 5). The PRISMA flow diagram is presented in Figure 1. Moreover, details of the search strategy and variations of the key search terms are presented in Figure 1.

As shown in Figure 2 and Table S1 in Multimedia Appendix 6, annual publication trends in SMM exhibited a consistent upward trajectory over the past decade. Between 2015 and 2024, the annual number of PubMed-indexed articles averaged 17.4. By July 31, 2025, 15 publications had already been recorded. Using an ARIMA-based exploratory projection fitted to the annual series, we estimated approximately 32 publications for 2025, with a wide 95% prediction interval (18-45) reflecting uncertainty due to the short annual time series. Overall, these findings indicate sustained growth and underscore the growing scholarly interest in the field.

The distributions of countries, institutions, and journals are summarized in Table 1. The United States was the leading contributor (54/189, 28.6%), followed by China (27/189, 14.3%), Italy (11/189, 5.8%), and France (10/189, 5.3%). Among institutions, the University of Texas at Austin (5/189, 2.7%) and Kazan Federal University (3/189, 1.6%) appeared most frequently. The Journal of Medical Internet Research was the dominant journal (21/189, 11.1%), followed by the International Journal of Environmental Research and Public Health (7/189, 3.7%) and JMIR Public Health and Surveillance (7/189, 3.7%). Beyond these core contributors, a long tail of less-represented countries, institutions, and journals reflects the field’s broadening boundaries and thematic heterogeneity.

The author collaboration network in Figure S3 in Multimedia Appendix 1 displays a pattern in which a few highly connected researchers, such as Graciela Gonzalez-Hernandez, Davy Weissenbacher, and Abeed Sarker, occupy central hub positions, maintaining multiple cross-institutional collaborations. Their cooperative studies are summarized in Tables S2 and S3 in Multimedia Appendix 6, highlighting broader collaboration portfolios, notably those of Nathalie Texier and colleagues. Overall, the field is shaped by a small number of core investigators complemented by a long tail of occasional contributors. Importantly, this authorship structure is mirrored in citation patterns, which further reflect the influence and reach of these core groups.

Citation analyses highlighted both globally influential work and highly interconnected contributions within the field. As shown in Figure S4 in Multimedia Appendix 1, the ranking of publications by RCR identified a set of cornerstone articles, with articles by Ayyoubzadeh et al [38] on COVID-19 trend prediction and Nikfarjam et al [39] on adverse drug reactions consistently leading the field. Other high-RCR papers addressed vaccine hesitancy, mental health, and methodological advances, such as machine learning (ML) and natural language processing (NLP), reflecting both topical diversity and methodological innovation among the most impactful studies. Within the internal citation network (Figure S5 in Multimedia Appendix 1), the article by Nikfarjam et al [39] again occupied the central position with the most citations, followed by articles by Tapi Nzali et al [40] on breast cancer and Lazard et al [41] on public communication. Several COVID-19–related studies (eg, Li et al [42] and Zhang et al [43]) also ranked prominently, underscoring the pandemic’s role in driving recent scholarly influence.

The keyword statistics in Table 2 and Figure S6 in Multimedia Appendix 1 revealed both stable core terms and dynamically emerging concepts. As shown in Figure S6 in Multimedia Appendix 1, “social media,” “data mining,” and “natural language processing” consistently ranked among the most frequent keywords forming a foundation in SMM. In contrast, the occurrence of terms, such as “COVID-19,” “vaccine hesitancy,” and “mental health,” increased sharply in specific years, highlighting the responsiveness of the literature to external events and public health concerns. Certain topics (eg, “sentiment analysis”) extended beyond 2025, suggesting potential growth.

We applied the Kleinberg burst detection algorithm to capture topic dynamics. The resulting heatmap (Figure 3) and timeline (Figure 4) identified multiple high-intensity bursts, with peak activity observed in 2020‐2021. Burst events were not limited to pandemic-related terms, and method-focused keywords, such as “topic modeling” and “ML,” indicated that methodological innovation engages in dynamic interaction with real-world demands.

We divided the study period into 3 slices using the explosive growth time stamp as the temporal boundary: 2015‐2019, 2020‐2023, and 2024-2025 (July 31, 2025). For each time slice, we scanned K∈[2.20]. The Silhouette and Calinski-Harabasz (CH) indices were noninformative in the spectral space (yielding values near zero or remaining constant), and thus, we prioritized modularity and used the Davies-Bouldin Index elbow as a safeguard against overfragmentation. The decision to fix the number of spectral clusters at K=3 (2015‐2019), K=8 (2020‐2023), and K=5 (2024‐2025) was guided by systematic evaluations across multiple criteria. While the Silhouette and CH indices were largely uninformative in the spectral embedding space, modularity consistently exhibited clear local maxima at these values, and Davies-Bouldin curves indicated elbows in the same vicinity. Selecting K at these points avoided both undersegmentation and overfragmentation while maximizing structural separation and interpretability. This choice ensured that each slice captured the dominant thematic structure of the period—technology-driven methodological consolidation before 2020, problem-driven thematic diversification from 2020 to 2023, and thematic convergence and strategic priorities after 2023. Figures S11-S14 in Multimedia Appendix 1 reveal the thematic changes across the 3 time slices.

We conducted sensitivity analyses on burst detection and clustering hyperparameters to validate the methods and findings at the macro-level.

In the parameter sensitivity analysis for burst detection, we selected the configuration with γ=0.5, a slice width of 3 years, and a minimum burst length of 1, which demonstrated strong overall performance. This setting achieved optimal values in coverage (1.0) and realism (1.8), ensuring that detected bursts were both comprehensive and plausible. Although its standalone stability scores (Jaccard=0.108; Spearman=0.259) appeared modest, cross-parameter comparisons (Table S9 in Multimedia Appendix 6) revealed that alternative configurations yielded Jaccard similarities ≥0.97 and Spearman correlations of approximately 0.70 with the baseline, confirming that the results are consistently reproducible across the parameter space. Thus, G0.5_W3_L1 was chosen as the primary setting.

The internal metrics for clustering evaluation are presented in Table S10 in Multimedia Appendix 6. As can be seen from the table, the balanced version with K=6 showed slightly better internal separation perception. In contrast, the fine version maintained “good” performance across major metrics while improving interpretability by an order of magnitude. Additionally, this study conducted 3 independent runs of fine_K6, all yielding nearly identical scores and key metrics. This indicates that the performance of fine_K6 is robust and insensitive to minor feature enhancements.

To assess the external validity of our clustering, we examined the relationship involving keyword network centrality and compared RCR distributions across clusters. As shown in Figure S15 in Multimedia Appendix 1, keyword strength was significantly positively correlated with mean RCR (Spearman ρ=0.49; P<.05), indicating that keywords occupying more central positions in the co-occurrence network tended to be associated with a higher citation impact (Figure S15 in Multimedia Appendix 1). Moreover, RCR distributions varied substantially across clusters (Figure S15 in Multimedia Appendix 1). Cluster 2 exhibited the highest citation impact (median RCR of approximately 1.5, with maximum values approaching 3), clearly exceeding other clusters. In contrast, clusters 1 and 4 showed lower mean RCR values (approximately 0.5‐0.7), while noise (cluster 1) had the lowest citation impact (mean RCR of approximately 0.45). Together, these findings demonstrate that the identified clusters not only reflect structural differences within the keyword network but also align with external citation impact, thereby validating the robustness and scientific relevance of our approach.

To enhance the interpretability of the clusters beyond quantitative indicators, we performed micro-level interpretive triangulation by systematically analyzing the introduction, contribution, limitations, and future directions reported in 28 representative studies (Multimedia Appendix 4). The content analysis suggested that the thematic boundaries identified by clustering correspond closely to coherent conceptual domains. First, studies assigned to cluster 2 (NLP/ML-driven social media analysis) consistently emphasized methodological innovation. This validates the cluster’s positioning as a methodological core in the strategic coordinate map where ML and NLP occupy high-influence and high-novelty regions [39,44]. Second, studies linked to clusters 1 and 2 highlighted empirical applications such as psychological health and suicide-related discussions [9,45]. Their reported contributions emphasized the utility of social media for monitoring population health behaviors, aligning with our dynamic slicing results where such content clusters emerged strongly during the COVID-19 period (2020‐2023) and then diversified into related subthemes. Third, the limitations articulated in these studies mirror the structural signals captured by clustering. Frequent reliance on Twitter and US-based data explains why Twitter appeared as a central hub in cluster 2 [7,46], while non-Twitter platforms (eg, Weibo, Facebook, and Google Maps reviews) were less frequently studied, corresponding to their marginal or emerging placement in the coordinate map. Similarly, methodological constraints in sentiment analysis, cross-lingual generalizability, and demographic representativeness corroborated the lower maturity scores of several peripheral clusters. Finally, the future research directions proposed by the authors include cross-platform data integration [7,46]. Deep learning methods, population stratification, and longitudinal prediction directly overlap with the potential breakthroughs located in the high-influence but low-maturity quadrants of our strategic map (eg, vaccine attitudes, digital health, and Weibo-based research). This convergence suggests that our framework not only captures the current structure of the field but also anticipates trajectories already envisioned by domain experts.

Collectively, this micro-level evidence mapping supports the interpretability of the thematic clusters and provides qualitative context that complements the quantitative indicators and external validation results, without being used as confirmatory evidence of model correctness. It also demonstrates that the clusters are semantically meaningful and consistent with the intellectual agenda of the field, thereby strengthening confidence in both the static and dynamic analyses.

This study used a hybrid methodology that combined ML-based clustering with qualitative evidence mapping to systematically draw a strategic research landscape of SMM. The findings clearly demonstrated that this field has evolved into a complex and coherent ecosystem. This ecosystem revolves around “infodemiology” as its absolute core, powered by a dual engine of “computational methods” and “analytical techniques,” extending its influence toward multiple critical application frontiers, including pharmacovigilance, public listening, and health management. Our findings not only depict the current state of the field but also reveal its underlying driving mechanisms and future developmental trajectory.

This study systematically analyzed health-related SMM literature collected in PubMed from 2015 to mid-2025, revealing significant evolutionary trends over the past decade. Overall, publication output showed steady growth, with explosive increases from 2019 to 2020, indicating SMM’s rising strategic importance as a tool for digital health and public health research. By integrating natural language model embeddings with network algorithm clustering, we identified 6 relatively independent yet interconnected thematic clusters. These spanned methodological anchors (NLP, ML, and social network analysis), socially driven topics (vaccine hesitancy and public listening), and priority themes reconverging in the postexplosive growth phase (health communication, mental health, and oncology). The distribution of these themes across a strategic 3D coordinate system (maturity, influence, and recency) further revealed the historical accumulation, current impact, and future potential of different research directions.

Time-slice analysis indicated that the knowledge structure of this field underwent three phased transitions: (1) the methodological consolidation from 2015 to 2019 laid the technical foundation; (2) the pandemic-driven surge from 2020 to 2023 accelerated thematic diversification, concentrating on various public health issues; and (3) by 2024‐2025, research refocused and converged on long-term strategic issues like cancer care and mental health. Through multiple validations, this study not only confirmed the robustness of clustering and strategic positioning but also demonstrated that these patterns align closely with the field’s actual development trajectory. Collectively, the study’s key findings provide a dynamic knowledge map for the digital health and health management field, spanning macro trends to micro evidence and offering a robust strategic reference for academic research, policy formulation, and practical applications.

Our finding that NLP and ML constitute the backbone of SMM aligns with recent reviews cataloging the rapid growth of computational methods in digital health [47-49]. However, some overviews have framed NLP as experimental in health care [50,51]. Our findings empirically demonstrate that, from a structural perspective, these technological pipelines have become firmly established as core architectures for social media–based health analytics (Table 3 and Figure 6). Additional empirical studies reinforce this interpretation. For instance, Ren et al [52] demonstrated the effectiveness of NLP and emotion-based deep learning (ML) for depression detection on Reddit, while Low et al [53] used NLP to identify vulnerable mental health support groups in online communities. Based on the above examples, these applications illustrate that methods are not only conceptually important but also practically operationalized in diverse health contexts. However, such research is often constrained by single-platform dependency and insufficient sample representativeness [52,54,55]. This is precisely why this strategic map positions this cluster as mature but still requiring methodological improvement and extended applications. Its strategic implications, from a sociological perspective, suggest that the dominance of a single-language Twitter, Facebook, Reddit, or Weibo corpus may structurally exclude speakers of other languages and marginalized voices, thereby exacerbating health inequalities [56-58]. From a health management perspective, mature NLP and ML pipelines are now deployable for real-time pharmacovigilance and attitude monitoring (eg, vaccine hesitancy dashboards), shifting management challenges from feasibility to governance [59-62]. Public health practice can benefit from earlier detection of emerging risks, enabling targeted, cost-effective preventive measures that reduce systemic burdens like disease incidence costs [63,64]. Collectively, the fundamental methods serve as strategic infrastructure in SMM.

Moreover, our analysis captured the explosion of crisis-driven research themes since 2020. Research during this period exhibited pronounced diversification and fragmentation, characterized by the rapid emergence of numerous exploratory subfields. Previous research accurately commented on this explosive growth but fragmented landscape in SMM [65]. However, to our knowledge, we provide data-driven evidence of an evolutionary trend in this field, shifting from fragmentation toward a focus on solving priority problems, based on our burst detection and time-slice analysis. Starting in 2024, as shown in our time-slice analysis, SMM research entered an era of integration characterized by a focus on addressing critical health issues. This signifies the field’s transition from an exploratory phase of data-driven research to a mission-driven phase oriented toward identifying problems to solve. While acknowledging the fragmented explosion as a necessary phase, we critically highlight the ongoing structural shift toward a more mature research paradigm.

Another major contribution of this study lies in methodological innovation. Unlike previous bibliometric research that relied primarily on co-occurrence–based keyword analysis, we proposed a fully automated, reproducible multilevel hybrid bibliometric methodology [17,18,24,29]. This approach of embedding-based mapping significantly enhances the interpretability and strategic value of the results while ensuring transparency and robustness [66].

First, at the semantic level, we used 2 natural language models—SPECTER2 and PubMedBERT—to vectorize titles and abstracts, aggregating them at the keyword level [66-68]. This approach overcomes the limitations of traditional methods that rely solely on manual synonym merging, enabling topic identification to capture latent semantic patterns and contextual associations [30]. In addition, at the structural level, we combined keyword co-occurrence networks with embedded similarity. Building upon the foundation, we constructed a hybrid similarity matrix that better aligns with actual disciplinary structures, achieving dual integration of semantics and structure.

Second, we used the nonparametric UMAP-HDBSCAN method to classify research themes. Systematic parameter grid searches and multiple random seed runs ensured the stability of clustering outcomes [69,70]. Unlike most studies relying solely on single clustering outputs, we further conducted internal (Jaccard, Silhouette, and Adjusted Rand Index) and external (RCR) validation. This was supplemented by micro-level evidence mapping and content analysis of representative papers. Such double validation could enhance clustering reliability.

Finally, this study proposes a 3D strategic coordinate map. By mapping clustering results onto the axes of maturity, influence, and recency, this framework transforms clustering outcomes into a knowledge map with strategic implications. This approach provides an intuitive tool for identifying hotspots, emerging frontiers, and declining fields.

Based on the above analysis, we identified several key research gaps and propose the following future research directions:

The outlined future directions can be consolidated into a transformable research framework. At the data level, comparative analyses involving cross-databases, multilingual datasets, and cross-platform sources should be encouraged to mitigate representational biases arising from single-platform or single-language sources while testing the transferability of thematic structures [78]. At the methodological level, semantic-structural integration can expand from static co-occurrence networks to dynamic networks to better capture thematic evolution and cross-cluster migration [79]. At the validation level, time slices and burst detection results can be compared against external reference data, such as policy timelines and significant events, while exploring event study methods to enhance interpretability and real-world applicability [73,75,80].

This study has several limitations. First, our analysis examined only English literature from PubMed, excluding other databases and other languages, which may not fully capture SMM applications across the entire field. In particular, this study relied on PubMed as its sole primary data source, resulting in a bias toward biomedical and clinical research. Consequently, representation from other fields was insufficient, potentially underestimating the scale of methodological innovations or interdisciplinary research. Second, due to the time lag in literature collection, recency may be underestimated. Third, the clustering analysis itself relied on keywords. While it considered the meaning of keywords within abstracts, it could not capture all the fine distinctions across studies. Moreover, we used keywords as analytical units in this study. Keywords themselves exhibit variations in naming and granularity, and have nonuniform usage. Hence, synonymous expressions may not be fully normalized. Although this study used PubMedBERT and SPECTER2 to learn structural and semantic similarity from title and abstract contexts, this approach only partially mitigates the fragmentation caused by relying on string matching. It cannot guarantee complete unification of all synonyms and polysemous terms, particularly when interdisciplinary terminology and task definitions are inconsistent. Beyond this, we did not conduct stratified external validation for each of the 3 time periods, such as comparing RCRs stage by stage. Such analyses may be statistically unstable with smaller samples and when reference metrics require time accumulation, potentially introducing systematic bias for the most recent stage, particularly the short and incomplete window of 2024‐2025 (July 2025). Future studies may explore stage-by-stage external validation with longer follow-up intervals. Additionally, SMM research can be influenced by structural biases associated with social media platforms and their user demographics [81]. For example, different platforms have different user demographics, and regional disparities and the digital divide may also affect the visibility of research topics. Our included SMM literature often relied on data from platforms like Twitter, Reddit, and Weibo, which differ in user profiles, languages, and geographic distributions. Consequently, which topics receive more research attention largely depends on the ease of data accessibility and user activity on these platforms. Therefore, the research findings presented here should primarily be understood as reflecting the knowledge structure at the level of academic publications, rather than being directly representative of the complete picture in the real world. Finally, as a bibliometric analysis, this study aimed to reveal correlations and trends rather than validate strict causal relationships.

This study systematically revealed the knowledge structure, strategic positioning, and evolutionary trajectory of health-related SMM from 2015 to 2025 by developing a fully automated, reproducible hybrid bibliometric methodology. The innovation of this study lies in constructing a semantic-structural hybrid similarity matrix while enhancing method robustness through dual-level validation to reduce synonym fragmentation and parameter sensitivity. Unlike traditional bibliometric reviews that primarily rely on co-occurrence statistics or single-pass clustering, this study offers a decision-oriented, comprehensive analysis integrating interpretable thematic clustering, 3D strategic positioning, intercluster relationships, and time-slice analysis. Substantively, we found that the research evolved through 3 distinct periods: an initial phase of methodological consolidation, a middle phase where themes were rapidly diversified and fragmented, and a later phase of long-term priority problem-solving, such as mental health and cancer care. The 3D strategic coordinate map further indicates that differences in maturity, influence, and recency among various themes determine their potential and challenges for future development.

Our findings hold practical implications for public health, health research, and health care. For public health, the findings confirm the necessity of infodemiology and provide methodological support for developing precise, audience-segmented public health communication strategies. For health research, the knowledge map offered by this study can be used to strategically set research priorities and optimize the allocation of research resources. For health care, technologies in this field can be applied to support real-world health surveillance systems and data-driven decision-making. In particular, mature analytic pipelines in SMM can support not only the development but also the validation of real-world surveillance and pharmacovigilance workflows and a better understanding of the patient journey through analysis of social media datasets, thereby supporting evidence-informed decision-making. Although this study provides an organized framework, further efforts are needed to support real-world decision-making. Regarding future work, the hybrid analytical framework proposed in this study will undergo continuous iteration across 3 dimensions. At the data level, we will conduct cross-database, multilingual, and cross-platform comparative analyses. Methodologically, we will extend semantic-structural integration from static co-occurrence networks to dynamic networks. For validation, we will combine temporal slicing analysis and burst detection with event analysis to enhance explainability and practical utility.