Emerging Research Trends and Thematic Structure of Post-Pandemic Post-Traumatic Stress Disorder (PTSD) A Bibliometric and BERTopic Based Topic Modeling Analysis

Emerging Research Trends and Thematic Structure of Post-Pandemic Post-Traumatic Stress Disorder (PTSD) A Bibliometric and BERTopic Based Topic Modeling Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Emerging Research Trends and Thematic Structure of Post-Pandemic Post-Traumatic Stress Disorder (PTSD) A Bibliometric and BERTopic Based Topic Modeling Analysis Y. Cheng Lin, Y. Ting Chang, Y. Hsuan Lin, Y. Chen Li, T. Ling Chiu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497724/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Post-traumatic stress disorder (PTSD) remains a central concern in psychiatry because of its significant heterogeneity and complex comorbidity patterns, which complicate both diagnosis and treatment. This study employs bibliometric analysis of literature from the Web of Science (WoS) database, identifying 26 Highly Cited Papers as core references for detailed examination. The aim is to map the thematic structure and evolving research trends within PTSD scholarship. Application of the BERTopic model to construct topic distributions revealed three primary research foci. The first group, Topics 0, 1, and 4, addresses emergency psychological responses during the COVID-19 pandemic (COVID-19, 0.084), with a particular focus on prevalence rates and mental health outcomes among student populations. The second, Topic 2, examines diagnostic and therapeutic strategies, emphasizing the interactive effects of genetic factors (genetic, 0.060), environmental influences (environmental, 0.041), and alexithymia (0.037). The third, Topic 3, investigates alcohol use (0.145) and substance use, highlighting the comorbidity challenges between PTSD and substance use disorders (SUDs). These findings indicate that future research and clinical practice should prioritize risk stratification and integrative treatment strategies based on gene–environment interaction (G×E) models to more effectively address the complex demands of the post-pandemic era. Psychology Psychiatry Post-Traumatic Stress Disorder (PTSD) Bibliometric analysis Comorbidity (Substance Use Disorders SUDs) Gene–Environment Interaction (GxE) Figures Figure 1 Figure 2 1 Introduction Post-traumatic stress disorder (PTSD) is a complex and severe mental disorder that results from direct exposure to, witnessing, or learning about events involving actual or threatened death, serious injury, or sexual violence (Koenen et al., 2017 ). Global epidemiological evidence indicates that exposure to potentially traumatic events is highly prevalent, with most individuals experiencing at least one such event during their lifetime (Kessler et al., 2017 ). However, only a subset of trauma-exposed individuals develop PTSD, which highlights the intricate interplay between individual vulnerability and environmental stressors (Smoller, 2016 ). Data from the World Health Organization (WHO) World Mental Health Surveys demonstrate substantial cross-national and cross-cultural variation in the lifetime prevalence of PTSD, underscoring heterogeneity in both diagnostic expression and underlying psychopathology across cultural contexts (Koenen et al., 2017 ; Kessler et al., 2017 ). Following the revision of PTSD diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the internal symptom structure of PTSD has become a central focus of psychopathological research. The DSM-5 expanded the diagnostic framework from three to four major symptom clusters, encompassing 20 specific symptoms, including intrusive symptoms, persistent avoidance, negative alterations in cognition and mood, and marked alterations in arousal and reactivity (Armour et al., 2015 ). This reconceptualization reflects increased recognition of the disorder’s multidimensional nature and has prompted renewed efforts to clarify symptom heterogeneity and underlying mechanisms. Within developmental psychopathology, childhood maltreatment and early-life trauma exposure are recognized as significant risk factors for the development of depression and anxiety disorders in adulthood. Empirical evidence demonstrates robust associations between maltreatment, as measured by the Childhood Trauma Questionnaire (CTQ), and depressive symptomatology (Humphreys et al., 2020 ). Conversely, social support shows a significant negative association with PTSD symptoms and serves as an effective psychological buffer, mitigating the adverse mental health consequences of traumatic experiences (Wang, 2021). Additionally, large-scale stressors such as the COVID-19 pandemic are conceptualized as collective traumatic events, contributing to increased PTSD symptoms among the general population and specific high-risk groups, including healthcare workers and perinatal women (Forte et al., 2020 ; Sun et al., 2021 ). Although evidence-based psychotherapies such as Cognitive Processing Therapy (CPT) and Prolonged Exposure therapy remain first-line treatments for PTSD, a substantial proportion of patients do not achieve full remission following these interventions (Burback et al., 2024 ). As a result, the investigation of adjunctive or alternative treatment approaches has become increasingly important. Given the complexity of PTSD symptom structures and the diversity of implicated risk factors, this study employs bibliometric analysis to systematically synthesize core literature and identify the structured thematic landscape of contemporary PTSD research across three major domains: collective trauma and emergency response, etiological mechanisms, and the management of complex comorbidities. 2 Literature Review 2.1 Diagnosis and Conceptual Framework of Post-Traumatic Stress Disorder The diagnostic conceptualization of post-traumatic stress disorder (PTSD) has continuously evolved alongside revisions of the Diagnostic and Statistical Manual of Mental Disorders (DSM). With the release of DSM-5, the symptom clusters of PTSD were restructured from the three-factor model in DSM-IV—re-experiencing, avoidance/numbing, and hyperarousal—into a four-factor model comprising intrusion symptoms, persistent avoidance, negative alterations in cognitions and mood (NACM), and marked alterations in arousal and reactivity (AAR) (Armour et al., 2015 ; Foa et al., 2015 ). Regarding the latent factor structure of PTSD, multiple theoretical models have been proposed. Armour et al. ( 2015 ) used confirmatory factor analysis (CFA) based on DSM-5 symptom criteria and supported the Hybrid Anhedonia and Externalizing Behaviors model. This seven-factor model further decomposes the four DSM-5 symptom clusters into intrusion, avoidance, negative affect, anhedonia, detachment, hyperarousal, and reckless or self-destructive behavior. Additionally, a cross-cultural, multi-site study demonstrated the replicability and generalizability of the PTSD symptom network structure, suggesting relative stability of symptom organization across populations and cultural contexts (Fried et al., 2018 ). To facilitate assessment under the DSM-5 framework, Foa et al. ( 2015 ) developed and validated the Posttraumatic Diagnostic Scale for DSM-5 (PDS-5), providing a psychometrically sound instrument for both clinical practice and research applications. 2.2 Epidemiology and Comorbidity of PTSD Exposure to traumatic events is highly prevalent worldwide; however, only a proportion of exposed individuals subsequently develop PTSD. Findings from the World Mental Health Surveys indicate that the lifetime prevalence of PTSD varies substantially across countries and trauma types, highlighting sociocultural differences in vulnerability and risk (Kessler et al., 2016; Koenen et al., 2017 ). High rates of psychiatric comorbidity are a defining clinical characteristic of PTSD, with anxiety and depressive disorders being the most common co-occurring conditions. These patterns indicate that traumatic experiences elicit persistent fear and avoidance responses and disrupt emotional regulation and cognitive functioning. During the COVID-19 pandemic, numerous studies reported that individuals exhibiting PTSD symptoms frequently experienced concurrent anxiety and depressive symptoms (Kar et al., 2021 ; Liu et al., 2020 ; Liu et al., 2021 ; Sun et al., 2021 ; Sousa et al., 2021). Similarly, intensive care unit (ICU) survivors exposed to prolonged social isolation and uncertainty display comparable psychological burdens, including intrusive recollections, sleep disturbances, helplessness, and persistent depressive symptoms during recovery (Hatch et al., 2018 ; Parker et al., 2015 ). These comorbid conditions hinder psychological and physical recovery and substantially increase the complexity of clinical management. 2.3 Risk and Protective Factors Associated with PTSD The development of PTSD is a multifaceted process resulting from the interaction of multiple risk and protective factors, rather than a single causal pathway. From a biological perspective, genetic factors are critical. PTSD, major depressive disorder (MDD), and anxiety disorders exhibit moderate heritability and share stress-related genetic vulnerability (Smoller, 2016 ). At the social level, environmental factors are essential. For instance, social support networks have been shown to reduce the risk of developing PTSD, as evidence indicates that higher social support predicts lower PTSD symptoms over time (Wang et al., 2021 ). In contrast, sustained or occupation-specific stressors can increase PTSD risk. Among frontline healthcare workers, moral injury and occupational burnout are closely associated with PTSD symptoms. During the early phase of the COVID-19 pandemic, frontline medical personnel who experienced moral distress exhibited significantly greater PTSD symptom severity, burnout, and psychosocial dysfunction (Norman et al., 2021 ; Restauri & Sheridan, 2020 ). 2.4 PTSD Research in Specific Traumatic Contexts The COVID-19 pandemic is a global health crisis and traumatic stressor (Bridgland et al., 2021 ), profoundly affecting mental health. Studies in China (Sun et al., 2021 ), Italy (Forte et al., 2020 ), and the US (Liu et al., 2020 ; Liu et al., 2021 ) found significant increases in PTSD symptoms. Subgroups especially affected include young adults, single people, those with higher education, and students (Kar et al., 2021 ; Liu et al., 2020 ). In perinatal women, grief and COVID-19-related health worries were key risk factors for depression, anxiety, and PTSD (Liu et al., 2021 ). For patients who survive critical illness and intensive care unit (ICU) treatment, the experience may constitute a lasting psychological trauma. Research on ICU survivors indicates that many continue to experience PTSD symptoms after discharge, which are associated with memory gaps, traumatic recollections, and specific physiological or treatment-related factors encountered during ICU hospitalization (Parker et al., 2015 ). Notably, elevated rates of anxiety, depression, and PTSD persist up to one year following ICU discharge (Hatch et al., 2018 ). Childbirth may also represent a traumatic experience for some women. Prenatal depressive symptoms, intense fear of childbirth, pregnancy-related complications, pre-existing PTSD, or prior engagement in psychological counseling strongly predict the development of childbirth-related PTSD in the postpartum period (Ayers et al., 2016 ). Among military veterans, traumatic brain injury (TBI) has been identified as a potential contributor to the development of psychogenic nonepileptic seizures (PNES). Mild TBI is highly associated with PNES diagnoses, and PTSD may play a critical moderating role in the progression from TBI exposure to PNES manifestation among veterans (Salinsky et al., n.d.). 2.5 Emerging Trends in PTSD Treatment In the treatment of PTSD, cognitive behavioral therapies (CBT), particularly Prolonged Exposure (PE) and Cognitive Processing Therapy (CPT), are widely regarded as gold-standard interventions. Nevertheless, ongoing research continues to explore novel therapeutic approaches, as a substantial proportion of patients do not achieve full remission following first-line treatments (Burback et al., 2024 ). Mindfulness-based interventions have been studied as adjunctive or alternative treatments for PTSD, though meta-analyses indicate mixed outcomes and variability in the quality and types of interventions reviewed (Mindfulness-Based Interventions for Psychological Trauma and Posttraumatic Stress Disorder (PTSD), 2025). Approaches such as mindfulness-based stress reduction and mindfulness-based cognitive therapy demonstrate relatively low dropout rates and moderate to large treatment effects in PTSD populations. Mindfulness interventions may provide therapeutic benefits by regulating emotional under- or over-modulation, processes central to PTSD symptomatology. Additionally, these interventions may facilitate the restoration of large-scale brain network connectivity, including interactions among the default mode network, central executive network, and salience network (Boyd et al., 2018 ). 3 Methodology 3.1 Research Design This study combines bibliometric analysis with neural topic modeling to systematically map the knowledge structure and research hotspots in post-traumatic stress disorder (PTSD). Large-scale quantitative analysis enables efficient processing of extensive academic literature and supports the identification of latent statistical relationships and long-term trends. This approach addresses the limitations of traditional qualitative or small-sample methods in capturing macro-level developments within the research domain. BERTopic is an advanced topic modeling framework that integrates semantically rich document embeddings from pretrained language models (BERT), nonlinear dimensionality reduction (Uniform Manifold Approximation and Projection, UMAP), and density-based clustering (Hierarchical Density-Based Spatial Clustering of Applications with Noise, HDBSCAN) (Compton, 2025 ). A class-based term frequency–inverse document frequency (c-TF-IDF) procedure is then used to extract semantically coherent and well-differentiated topics (Grootendorst, 2022 ). This hybrid architecture enables precise clustering and preserves high-dimensional semantic information, resulting in clearer and more discriminative topic representations. The research design consists of two complementary stages. First, a comprehensive comparative analysis of three topic modeling approaches, including BERTopic, is conducted to quantitatively identify the thematic structure of the research landscape. Second, an in-depth qualitative synthesis uses highly cited core articles to validate and interpret the quantitative topic modeling results (Kafiabad et al., 2025). The analysis focuses on three core domains: collective trauma and emergency responses related to COVID-19, etiological mechanisms underlying PTSD diagnosis and treatment, and management challenges associated with complex comorbidities, particularly PTSD and substance use disorders (SUDs). This integrated approach provides empirical insights for future clinical practice and scientific research. 3.2 Research Process The bibliometric topic analysis employed in this study follows a three-stage research process: literature identification and data acquisition, text preprocessing, and topic extraction and visualization (Fig. 1 ). 3.2.1 Literature Identification and Data Extraction The Web of Science (WoS) Core Collection database serves as the primary data source for this study, selected for its comprehensive coverage of academic publications relevant to PTSD research. The WoS database’s rigorous indexing and selection criteria ensure broad disciplinary representation and high-quality scholarly records (Supporting integrity of the scholarly record: Our commitment to curation and selectivity in the Web of Science, 2023). The analysis focuses on article abstracts, which concisely summarize research objectives, methodologies, and key findings, making them well-suited for topic modeling applications. 3.2.2 Text Preprocessing All collected textual data were preprocessed to improve analytical accuracy and computational efficiency. The preprocessing pipeline followed guidelines recommended by Grootendorst ( 2022 ), the developer of BERTopic, and included the following steps, as described in Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques (2024, pp. 123–135): 1. Text Cleaning and Normalization: Non-semantic elements, including special characters, numerals, punctuation marks, and URLs, were removed from the text corpus. 2. Stop Word Removal: Commonly occurring words with low discriminative value, including prepositions, conjunctions, and auxiliary terms, were excluded to enhance topic distinctiveness. 3. Lemmatization: Words were reduced to their base or root forms to ensure semantic consistency and improve model recognition efficiency. 4. Embedding Generation: The cleaned and normalized texts were processed with a pretrained BERT model to generate high-dimensional, semantically rich document embeddings. 3.2.3 Topic Extraction and Visualization Topic extraction was conducted using the BERTopic framework through the following procedures: 1. Dimensionality Reduction: High-dimensional document embeddings were reduced using the Uniform Manifold Approximation and Projection (UMAP) algorithm. Density-Based Clustering. 2. Density-Based Clustering: Documents were clustered in the reduced embedding space using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), a density-based clustering algorithm that efficiently identifies stable clusters of varying shapes and sizes (Bot et al., n.d.). HDBSCAN can identify clusters of varying density and label documents that do not belong to any coherent topic as noise. This capability is especially important when analyzing highly heterogeneous and fragmented academic research landscapes (González-Alemán, 2022 , pp. 5191–5198). 3. Topic Representation and Naming: For each identified cluster, topic keywords were extracted using the class-based term frequency–inverse document frequency (c-TF-IDF) weighting scheme. The c-TF-IDF approach quantifies the importance of terms within specific clusters and generates representative topic word scores, supporting clear topic interpretation and labeling (Class-Based TF-IDF Procedure, 2025). 4 Result The BERTopic results, based on semantic similarity and clustering relationships among topics, delineate three core research foci: (1) complex comorbidity challenges and diagnostic heterogeneity, (2) collective trauma and emergency psychological responses, and (3) the etiological origins of PTSD. Table 1 presents the core keywords associated with each topic and their corresponding c-TF-IDF weights. Each keyword’s c-TF-IDF score indicates its discriminative power and relative importance within the topic. Table 1 Core keywords and c-TF-IDF weight distributions identified by the BERTopic model Research Focus Topic ID Core Keywords and c-TF-IDF Weights Complex comorbidity and diagnostic heterogeneity Topic 0, 4 alcohol (0.145), use (0.105), PTSD (0.075), disorder (0.052), substance (0.044), diagnostic (0.026), cannabis (0.037) Collective trauma and emergency response Topic 1, 2, 3 COVID (0.084/0.057/0.055), 19 (0.084/0.055/0.057), students (0.083/0.056), anxiety (0.063), sleep (0.045), depression (0.040) Etiological mechanisms (G×E) Topic − 1 genetic (0.060), environmental (0.041), alexithymia (0.037), association (0.046), disorder (0.043) 4.1 Complex Comorbidity and Diagnostic Heterogeneity (Topic 0, 4) Topic 0 (core diagnostic structure) and Topic 4 (substance use challenges) constitute a closely linked semantic cluster. Although PTSD is a well-defined psychiatric disorder (disorder, 0.052), current research predominantly addresses its primary clinical challenge: comorbidity with substance use disorders (SUDs). The notably high weights of alcohol (0.145) and use (0.105), which surpass the foundational term PTSD (0.075), offer robust quantitative evidence that SUD-related comorbidity is a central focus in both academic research and clinical practice (A systematic review and meta-analysis of psychological interventions for comorbid post-traumatic stress disorder and substance use disorder, 2022, pp. 1051–1061). The literature in this cluster addresses not only alcohol misuse but also the influence of specific substances such as cannabis (0.037) on the persistence of post-traumatic symptoms, often within the framework of the self-medication hypothesis. The inclusion of the keyword diagnostic (0.026) underscores ongoing scholarly emphasis on diagnostic precision and improvement. Although the DSM-5 four-factor structure—intrusion, avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity—remains foundational for clinical diagnosis, recent studies indicate that revisions may be necessary. Evidence supporting six- and seven-factor models suggests that the current structure may not adequately represent the complexity of PTSD symptomatology (A systematic literature review of PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM-5, 2016, pp. 302–310; Petri et al., 2022, pp. 149–159). The seven-factor Hybrid Anhedonia and Externalizing Behaviors model, which divides DSM-5 symptom clusters into intrusion, avoidance, negative affect, anhedonia, detachment, hyperarousal, and reckless or self-destructive behavior, has shown greater explanatory capacity for symptom heterogeneity across diverse populations (C et al., 2015, pp. 106–113). The BERTopic clustering results align with this theoretical perspective, as alcohol, substance, and cannabis are prominently represented. It is important to note that PTSD is more closely linked to anhedonia than most substance use disorders, except for opioid use disorder; however, anhedonia does not occur uniformly across all SUD comorbidities (Stull et al., 2022). These findings indicate that BERTopic identifies substance-related comorbidity as a significant research focus and reveals varying relationships between problematic substance use and PTSD, suggesting that comorbidity may be influenced by different factors depending on the specific substance involved (Comorbidity of problematic substance use and other addictive behaviors and anxiety, depression, and post-traumatic stress disorder: a network analysis, 2024, pp. 1050–1058). 4.2 Collective Trauma and Emergency Psychological Responses: Analysis of Topics 1, 2, and 3 Topics 1, 2, and 3 collectively establish a structured research agenda addressing collective trauma and emergency psychological responses. Elevated c-TF-IDF weights for 'COVID' (0.084) and '19' (0.084) indicate that COVID-19 represents a primary traumatic stressor at the macro level (COVID-19 as a traumatic stressor is an indicator of mental health symptomatology, 2021, pp. 1–5). These data demonstrate a rapid scholarly response to acute psychological stress reactions during the pandemic, as reflected by the weight for 'pandemic' (0.048) (Prevalence of posttraumatic and general psychological stress during COVID-19: A rapid review and meta-analysis, 2020). Expanding upon this macro-level perspective, the analysis focuses on younger populations. Students (0.083/0.056) are identified as a primary data source (The prevalence of psychological stress in student populations during the COVID-19 epidemic: a systematic review and meta-analysis, 2022). This emphasis reveals a significant scholarly interest in anxiety (0.063) and depression (0.040) comorbidities within this vulnerable group (Status and epidemiological characteristics of depression and anxiety among Chinese university students in 2023, 2023). Topic 3 further refines the analysis by identifying sleep disturbances (0.045) as a key symptom in emergency stress responses (Cellini et al., 2020 , pp. 300–307). Collectively, these findings underscore the necessity for precise measurement and targeted interventions addressing both psychological and physiological symptoms in large-scale traumatic contexts. 4.3 Etiological Mechanisms of PTSD (Topic − 1) Although Topic-1 is categorized as unassigned within the BERTopic framework, its core keywords delineate the theoretical frontier of PTSD research. These keywords primarily address disease susceptibility and etiological origins. The co-occurrence of genetic (0.060) and environmental (0.041) factors strongly supports the widespread adoption of gene–environment interaction (G×E) frameworks. Such frameworks elucidate individual differences in PTSD vulnerability and move beyond explanations based solely on trauma exposure. Additionally, alexithymia (0.037) has emerged as a transdiagnostic marker associated with disorder (0.043), highlighting growing research interest in emotion regulation deficits as a central mechanism in PTSD pathogenesis. This etiological emphasis provides a biopsychological foundation for refining treatment strategies and developing personalized intervention approaches. 5 Conclusion This study utilized BERTopic to systematically map the latent thematic structure and evolutionary patterns within post-traumatic stress disorder (PTSD) research. The resulting topic architecture confirms established findings in the literature and demonstrates a shift in contemporary scholarship toward emerging challenges and theoretical frontiers. Collectively, the findings indicate a transition in PTSD research from descriptive symptom documentation to integrative models that emphasize heterogeneity, comorbidity, and multilevel etiological mechanisms, thereby highlighting the field’s progression and current priorities. 5.1 Key Findings The BERTopic results identified a prominent cluster of COVID-19–related topics (Topics 1–3), reflecting a rapid and concentrated scholarly response to large-scale collective stressors. The prominence and relative weight of these topics indicate that PTSD research during public health crises has prioritized vulnerable populations, particularly students, and has increasingly focused on emotional comorbidities such as anxiety and depression. Notably, sleep-related disturbances emerged as a quantifiable and recurrent symptom dimension, underscoring their value as early indicators for risk stratification and timely psychological intervention in emergency contexts. A second major thematic axis highlights the persistent clinical challenge of PTSD comorbidity with substance use disorders (SUDs), as evidenced by the high salience of terms such as alcohol, use, and substance. Although the DSM-5 four-factor model remains the dominant diagnostic framework, the topic structure suggests that this model may not sufficiently capture clinically salient dimensions, particularly externalizing behaviors and anhedonia. In contrast, seven-factor or hybrid models appear better suited to explaining symptom heterogeneity across populations. The BERTopic clustering supports this perspective, as SUD-related themes converge with dimensions corresponding to externalizing behavior and anhedonia, thereby providing data-driven support for extended PTSD symptom models. Topic-1 delineates the theoretical frontier of PTSD research by focusing on gene–environment interactions (GxE) and alexithymia. The co-occurrence of genetic and environmental terms highlights the joint contribution of biological susceptibility and environmental stressors to PTSD vulnerability. Concurrently, alexithymia emerges as a transdiagnostic psychological marker, emphasizing deficits in emotional awareness and regulation. Rather than treating these mechanisms as independent, the findings suggest their conceptual integration: GxE processes provide a biological foundation of vulnerability, while alexithymia represents a psychological mechanism through which this vulnerability is expressed. Together, these factors offer a coherent explanation for the pronounced heterogeneity in PTSD symptom manifestation among individuals exposed to similar traumatic events. 5.2 Theoretical and Clinical Implications The bibliometric and topic modeling results provide empirical support for the growing consensus that multi-factor and hybrid PTSD models offer superior explanatory power compared to the traditional DSM-5 four-factor structure. By explicitly capturing dimensions such as externalizing behaviors and anhedonia, these models enhance conceptual clarity regarding symptom heterogeneity and comorbidity. The identification of youth populations and sleep disturbances as central themes in crisis-related PTSD research underscores their practical relevance as high-risk groups and measurable indicators during public health emergencies. These findings offer actionable guidance for rapid psychological screening, early intervention, and resource allocation. At the etiological level, the convergence of GxE mechanisms and alexithymia within the topic structure underscores their central role in shaping PTSD vulnerability and comorbidity patterns. This integrated perspective supports the development of precision-oriented risk stratification strategies and preventive interventions that address both biological susceptibility and emotional regulation deficits. 5.3 Limitations and Future Directions Despite its strengths, this study has several limitations. Although BERTopic effectively captures macro-level thematic structures and research trends within the Web of Science database, it does not permit causal inference at the individual level. Given these constraints, future research should extend the present findings in several directions. First, to address these gaps, the clinical efficacy of integrated treatment approaches targeting complex comorbid profiles, particularly PTSD with SUDs and alexithymia, should be evaluated through randomized controlled trials (RCTs). Such studies are essential for translating thematic insights into evidence-based interventions. Second, longitudinal research designs are needed to clarify the temporal sequencing and mediating pathways underlying GxE mechanisms in PTSD development. Sustained investment in longitudinal datasets would enable a more precise examination of how genetic vulnerability and environmental stressors interact over time. 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Traumatic brain injury and psychogenic nonepileptic seizures in veterans Smoller JW (2016) The genetics of stress-related disorders: PTSD, depression, and anxiety disorders Neuropsychopharmacology 41(1), 297–319 https://doi.org/10.1038/npp.2015.266 Sun L, Sun Z, Wu L, Zhu Z, Zhang F, Shang Z, Jia Y, Gu J, Zhou Y, Wang Y, Liu N (2021) Prevalence and risk factors of acute posttraumatic stress symptoms during the COVID-19 outbreak Journal of Affective Disorders, 283, 123–129 Wang Y, Di Y, Ye J, Wei W (2021) Study on the public psychological states and its related factors during the outbreak of coronavirus disease (COVID-19) Frontiers in Psychology, 12, 599899 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Cheng Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACNvbG5gcJP9h4+BFiCfi18PEcbjP42MMnJ9mArOUAHi1yEukNkjPY5IwNEKoIaGFjSGww5uExS9x87fABphs1hxn42XMMmD+24dNysOExj0Va4rbbaQnMOccOM0j2vDFgOIhPC2MjyJZjQC1Aw3MbDjMY3MgBatmGRwszY4M0D9v/xM2z8z+AtdgT1MLGCPI+m7GBdA4DxBYJQlp4GEGBzCYncTvN4HDOsXQeiTPPCg6c/Ydbi/z8548hUTk7+eHjnBprOf725I0PKs7g1oICDgAxD4wxCkbBKBgFo4ACAADATVNR3SbEMAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-7799-8274","institution":"Ming Chuan University","correspondingAuthor":true,"prefix":"","firstName":"Y.","middleName":"Cheng","lastName":"Lin","suffix":""},{"id":568193496,"identity":"d03d9647-5155-4e9e-b8a9-dffb41eaae12","order_by":1,"name":"Y. Ting Chang","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Y.","middleName":"Ting","lastName":"Chang","suffix":""},{"id":568193497,"identity":"11b30b98-965a-4a55-a1d5-c9955dcae9c2","order_by":2,"name":"Y. Hsuan Lin","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Y.","middleName":"Hsuan","lastName":"Lin","suffix":""},{"id":568193498,"identity":"925a38f5-1ca5-4341-8212-c1a8c8b9813f","order_by":3,"name":"Y. Chen Li","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Y.","middleName":"Chen","lastName":"Li","suffix":""},{"id":568193499,"identity":"84757899-162f-4efb-97b6-523e4e025565","order_by":4,"name":"T. Ling Chiu","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"Ling","lastName":"Chiu","suffix":""},{"id":568193500,"identity":"029f67bc-c698-4b2d-8b5f-be378080bca6","order_by":5,"name":"C. Han Wang","email":"","orcid":"","institution":"Ming Chuan University","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"Han","lastName":"Wang","suffix":""},{"id":568193501,"identity":"8b19941a-00e2-4eb9-aa40-291d6ece3555","order_by":6,"name":"C. 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09:01:23","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88124,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8497724/v1/e247fe04a7faea2648a6300a.html"},{"id":99508047,"identity":"9e50fa10-f465-4923-9972-727ba6010d05","added_by":"auto","created_at":"2026-01-05 09:01:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115721,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the BERTopic algorithm\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8497724/v1/51d7d1e50c3f3f4512dfe8f5.png"},{"id":99790980,"identity":"43b490c4-410c-473c-a422-2fadda571cc7","added_by":"auto","created_at":"2026-01-08 12:58:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103046,"visible":true,"origin":"","legend":"\u003cp\u003eBERTopic – Topic Word Scores\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8497724/v1/57e6b000ab075aa7e6c77b6a.png"},{"id":99803679,"identity":"70a8f148-ae6f-4a50-94f0-a3184caefe76","added_by":"auto","created_at":"2026-01-08 14:10:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8497724/v1/2891dc65-5c2a-450b-9738-355f5c1ef47d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEmerging Research Trends and Thematic Structure of Post-Pandemic Post-Traumatic Stress Disorder (PTSD) \u003cbr\u003e\nA Bibliometric and BERTopic Based Topic Modeling Analysis\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePost-traumatic stress disorder (PTSD) is a complex and severe mental disorder that results from direct exposure to, witnessing, or learning about events involving actual or threatened death, serious injury, or sexual violence (Koenen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Global epidemiological evidence indicates that exposure to potentially traumatic events is highly prevalent, with most individuals experiencing at least one such event during their lifetime (Kessler et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, only a subset of trauma-exposed individuals develop PTSD, which highlights the intricate interplay between individual vulnerability and environmental stressors (Smoller, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Data from the World Health Organization (WHO) World Mental Health Surveys demonstrate substantial cross-national and cross-cultural variation in the lifetime prevalence of PTSD, underscoring heterogeneity in both diagnostic expression and underlying psychopathology across cultural contexts (Koenen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kessler et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the revision of PTSD diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the internal symptom structure of PTSD has become a central focus of psychopathological research. The DSM-5 expanded the diagnostic framework from three to four major symptom clusters, encompassing 20 specific symptoms, including intrusive symptoms, persistent avoidance, negative alterations in cognition and mood, and marked alterations in arousal and reactivity (Armour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This reconceptualization reflects increased recognition of the disorder\u0026rsquo;s multidimensional nature and has prompted renewed efforts to clarify symptom heterogeneity and underlying mechanisms.\u003c/p\u003e \u003cp\u003eWithin developmental psychopathology, childhood maltreatment and early-life trauma exposure are recognized as significant risk factors for the development of depression and anxiety disorders in adulthood. Empirical evidence demonstrates robust associations between maltreatment, as measured by the Childhood Trauma Questionnaire (CTQ), and depressive symptomatology (Humphreys et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, social support shows a significant negative association with PTSD symptoms and serves as an effective psychological buffer, mitigating the adverse mental health consequences of traumatic experiences (Wang, 2021). Additionally, large-scale stressors such as the COVID-19 pandemic are conceptualized as collective traumatic events, contributing to increased PTSD symptoms among the general population and specific high-risk groups, including healthcare workers and perinatal women (Forte et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough evidence-based psychotherapies such as Cognitive Processing Therapy (CPT) and Prolonged Exposure therapy remain first-line treatments for PTSD, a substantial proportion of patients do not achieve full remission following these interventions (Burback et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, the investigation of adjunctive or alternative treatment approaches has become increasingly important. Given the complexity of PTSD symptom structures and the diversity of implicated risk factors, this study employs bibliometric analysis to systematically synthesize core literature and identify the structured thematic landscape of contemporary PTSD research across three major domains: collective trauma and emergency response, etiological mechanisms, and the management of complex comorbidities.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Diagnosis and Conceptual Framework of Post-Traumatic Stress Disorder\u003c/h2\u003e \u003cp\u003eThe diagnostic conceptualization of post-traumatic stress disorder (PTSD) has continuously evolved alongside revisions of the Diagnostic and Statistical Manual of Mental Disorders (DSM). With the release of DSM-5, the symptom clusters of PTSD were restructured from the three-factor model in DSM-IV\u0026mdash;re-experiencing, avoidance/numbing, and hyperarousal\u0026mdash;into a four-factor model comprising intrusion symptoms, persistent avoidance, negative alterations in cognitions and mood (NACM), and marked alterations in arousal and reactivity (AAR) (Armour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Foa et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the latent factor structure of PTSD, multiple theoretical models have been proposed. Armour et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used confirmatory factor analysis (CFA) based on DSM-5 symptom criteria and supported the Hybrid Anhedonia and Externalizing Behaviors model. This seven-factor model further decomposes the four DSM-5 symptom clusters into intrusion, avoidance, negative affect, anhedonia, detachment, hyperarousal, and reckless or self-destructive behavior. Additionally, a cross-cultural, multi-site study demonstrated the replicability and generalizability of the PTSD symptom network structure, suggesting relative stability of symptom organization across populations and cultural contexts (Fried et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To facilitate assessment under the DSM-5 framework, Foa et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) developed and validated the Posttraumatic Diagnostic Scale for DSM-5 (PDS-5), providing a psychometrically sound instrument for both clinical practice and research applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Epidemiology and Comorbidity of PTSD\u003c/h2\u003e \u003cp\u003eExposure to traumatic events is highly prevalent worldwide; however, only a proportion of exposed individuals subsequently develop PTSD. Findings from the World Mental Health Surveys indicate that the lifetime prevalence of PTSD varies substantially across countries and trauma types, highlighting sociocultural differences in vulnerability and risk (Kessler et al., 2016; Koenen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh rates of psychiatric comorbidity are a defining clinical characteristic of PTSD, with anxiety and depressive disorders being the most common co-occurring conditions. These patterns indicate that traumatic experiences elicit persistent fear and avoidance responses and disrupt emotional regulation and cognitive functioning. During the COVID-19 pandemic, numerous studies reported that individuals exhibiting PTSD symptoms frequently experienced concurrent anxiety and depressive symptoms (Kar et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sousa et al., 2021). Similarly, intensive care unit (ICU) survivors exposed to prolonged social isolation and uncertainty display comparable psychological burdens, including intrusive recollections, sleep disturbances, helplessness, and persistent depressive symptoms during recovery (Hatch et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These comorbid conditions hinder psychological and physical recovery and substantially increase the complexity of clinical management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Risk and Protective Factors Associated with PTSD\u003c/h2\u003e \u003cp\u003eThe development of PTSD is a multifaceted process resulting from the interaction of multiple risk and protective factors, rather than a single causal pathway. From a biological perspective, genetic factors are critical. PTSD, major depressive disorder (MDD), and anxiety disorders exhibit moderate heritability and share stress-related genetic vulnerability (Smoller, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the social level, environmental factors are essential. For instance, social support networks have been shown to reduce the risk of developing PTSD, as evidence indicates that higher social support predicts lower PTSD symptoms over time (Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, sustained or occupation-specific stressors can increase PTSD risk. Among frontline healthcare workers, moral injury and occupational burnout are closely associated with PTSD symptoms. During the early phase of the COVID-19 pandemic, frontline medical personnel who experienced moral distress exhibited significantly greater PTSD symptom severity, burnout, and psychosocial dysfunction (Norman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Restauri \u0026amp; Sheridan, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 PTSD Research in Specific Traumatic Contexts\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic is a global health crisis and traumatic stressor (Bridgland et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), profoundly affecting mental health. Studies in China (Sun et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Italy (Forte et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the US (Liu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found significant increases in PTSD symptoms. Subgroups especially affected include young adults, single people, those with higher education, and students (Kar et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In perinatal women, grief and COVID-19-related health worries were key risk factors for depression, anxiety, and PTSD (Liu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor patients who survive critical illness and intensive care unit (ICU) treatment, the experience may constitute a lasting psychological trauma. Research on ICU survivors indicates that many continue to experience PTSD symptoms after discharge, which are associated with memory gaps, traumatic recollections, and specific physiological or treatment-related factors encountered during ICU hospitalization (Parker et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, elevated rates of anxiety, depression, and PTSD persist up to one year following ICU discharge (Hatch et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChildbirth may also represent a traumatic experience for some women. Prenatal depressive symptoms, intense fear of childbirth, pregnancy-related complications, pre-existing PTSD, or prior engagement in psychological counseling strongly predict the development of childbirth-related PTSD in the postpartum period (Ayers et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong military veterans, traumatic brain injury (TBI) has been identified as a potential contributor to the development of psychogenic nonepileptic seizures (PNES). Mild TBI is highly associated with PNES diagnoses, and PTSD may play a critical moderating role in the progression from TBI exposure to PNES manifestation among veterans (Salinsky et al., n.d.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Emerging Trends in PTSD Treatment\u003c/h2\u003e \u003cp\u003eIn the treatment of PTSD, cognitive behavioral therapies (CBT), particularly Prolonged Exposure (PE) and Cognitive Processing Therapy (CPT), are widely regarded as gold-standard interventions. Nevertheless, ongoing research continues to explore novel therapeutic approaches, as a substantial proportion of patients do not achieve full remission following first-line treatments (Burback et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMindfulness-based interventions have been studied as adjunctive or alternative treatments for PTSD, though meta-analyses indicate mixed outcomes and variability in the quality and types of interventions reviewed (Mindfulness-Based Interventions for Psychological Trauma and Posttraumatic Stress Disorder (PTSD), 2025). Approaches such as mindfulness-based stress reduction and mindfulness-based cognitive therapy demonstrate relatively low dropout rates and moderate to large treatment effects in PTSD populations. Mindfulness interventions may provide therapeutic benefits by regulating emotional under- or over-modulation, processes central to PTSD symptomatology. Additionally, these interventions may facilitate the restoration of large-scale brain network connectivity, including interactions among the default mode network, central executive network, and salience network (Boyd et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study combines bibliometric analysis with neural topic modeling to systematically map the knowledge structure and research hotspots in post-traumatic stress disorder (PTSD). Large-scale quantitative analysis enables efficient processing of extensive academic literature and supports the identification of latent statistical relationships and long-term trends. This approach addresses the limitations of traditional qualitative or small-sample methods in capturing macro-level developments within the research domain.\u003c/p\u003e \u003cp\u003eBERTopic is an advanced topic modeling framework that integrates semantically rich document embeddings from pretrained language models (BERT), nonlinear dimensionality reduction (Uniform Manifold Approximation and Projection, UMAP), and density-based clustering (Hierarchical Density-Based Spatial Clustering of Applications with Noise, HDBSCAN) (Compton, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A class-based term frequency\u0026ndash;inverse document frequency (c-TF-IDF) procedure is then used to extract semantically coherent and well-differentiated topics (Grootendorst, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This hybrid architecture enables precise clustering and preserves high-dimensional semantic information, resulting in clearer and more discriminative topic representations.\u003c/p\u003e \u003cp\u003eThe research design consists of two complementary stages. First, a comprehensive comparative analysis of three topic modeling approaches, including BERTopic, is conducted to quantitatively identify the thematic structure of the research landscape. Second, an in-depth qualitative synthesis uses highly cited core articles to validate and interpret the quantitative topic modeling results (Kafiabad et al., 2025). The analysis focuses on three core domains: collective trauma and emergency responses related to COVID-19, etiological mechanisms underlying PTSD diagnosis and treatment, and management challenges associated with complex comorbidities, particularly PTSD and substance use disorders (SUDs). This integrated approach provides empirical insights for future clinical practice and scientific research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Process\u003c/h2\u003e \u003cp\u003eThe bibliometric topic analysis employed in this study follows a three-stage research process: literature identification and data acquisition, text preprocessing, and topic extraction and visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Literature Identification and Data Extraction\u003c/h2\u003e \u003cp\u003eThe Web of Science (WoS) Core Collection database serves as the primary data source for this study, selected for its comprehensive coverage of academic publications relevant to PTSD research. The WoS database\u0026rsquo;s rigorous indexing and selection criteria ensure broad disciplinary representation and high-quality scholarly records (Supporting integrity of the scholarly record: Our commitment to curation and selectivity in the Web of Science, 2023). The analysis focuses on article abstracts, which concisely summarize research objectives, methodologies, and key findings, making them well-suited for topic modeling applications.\u003c/p\u003e \u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Text Preprocessing\u003c/h2\u003e\n \u003cp\u003eAll collected textual data were preprocessed to improve analytical accuracy and computational efficiency. The preprocessing pipeline followed guidelines recommended by Grootendorst (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), the developer of BERTopic, and included the following steps, as described in Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques (2024, pp. 123\u0026ndash;135):\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Text Cleaning and Normalization:\u003c/h3\u003e\n\u003cp\u003eNon-semantic elements, including special characters, numerals, punctuation marks, and URLs, were removed from the text corpus.\u003c/p\u003e\n\u003ch3\u003e2. Stop Word Removal:\u003c/h3\u003e\n\u003cp\u003eCommonly occurring words with low discriminative value, including prepositions, conjunctions, and auxiliary terms, were excluded to enhance topic distinctiveness.\u003c/p\u003e\n\u003cp\u003e3. Lemmatization: Words were reduced to their base or root forms to ensure semantic consistency and improve model recognition efficiency.\u003c/p\u003e\n\u003cp\u003e4. Embedding Generation:\u003c/p\u003e\n\u003cp\u003eThe cleaned and normalized texts were processed with a pretrained BERT model to generate high-dimensional, semantically rich document embeddings.\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003cdiv class=\"Heading\"\u003e3.2.3 Topic Extraction and Visualization\u003c/div\u003e\n \u003cp\u003eTopic extraction was conducted using the BERTopic framework through the following procedures:\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Dimensionality Reduction:\u003c/h3\u003e\n\u003cp\u003eHigh-dimensional document embeddings were reduced using the Uniform Manifold Approximation and Projection (UMAP) algorithm. Density-Based Clustering.\u003c/p\u003e\n\u003ch3\u003e2. Density-Based Clustering:\u003c/h3\u003e\n\u003cp\u003eDocuments were clustered in the reduced embedding space using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), a density-based clustering algorithm that efficiently identifies stable clusters of varying shapes and sizes (Bot et al., n.d.). HDBSCAN can identify clusters of varying density and label documents that do not belong to any coherent topic as noise. This capability is especially important when analyzing highly heterogeneous and fragmented academic research landscapes (Gonz\u0026aacute;lez-Alem\u0026aacute;n, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, pp. 5191\u0026ndash;5198).\u003c/p\u003e\n\u003ch3\u003e3. Topic Representation and Naming:\u003c/h3\u003e\n\u003cp\u003eFor each identified cluster, topic keywords were extracted using the class-based term frequency\u0026ndash;inverse document frequency (c-TF-IDF) weighting scheme. The c-TF-IDF approach quantifies the importance of terms within specific clusters and generates representative topic word scores, supporting clear topic interpretation and labeling (Class-Based TF-IDF Procedure, 2025).\u003c/p\u003e"},{"header":"4 Result","content":"\u003cp\u003eThe BERTopic results, based on semantic similarity and clustering relationships among topics, delineate three core research foci: (1) complex comorbidity challenges and diagnostic heterogeneity, (2) collective trauma and emergency psychological responses, and (3) the etiological origins of PTSD. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the core keywords associated with each topic and their corresponding c-TF-IDF weights. Each keyword\u0026rsquo;s c-TF-IDF score indicates its discriminative power and relative importance within the topic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCore keywords and c-TF-IDF weight distributions identified by the BERTopic model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopic ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCore Keywords and c-TF-IDF Weights\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex comorbidity and diagnostic heterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopic 0, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ealcohol (0.145), use (0.105), PTSD (0.075), disorder (0.052), substance (0.044), diagnostic (0.026), cannabis (0.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollective trauma and emergency response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopic 1, 2, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID (0.084/0.057/0.055), 19 (0.084/0.055/0.057), students (0.083/0.056), anxiety (0.063), sleep (0.045), depression (0.040)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiological mechanisms (G\u0026times;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopic\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egenetic (0.060), environmental (0.041), alexithymia (0.037), association (0.046), disorder (0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Complex Comorbidity and Diagnostic Heterogeneity (Topic 0, 4)\u003c/h2\u003e \u003cp\u003eTopic 0 (core diagnostic structure) and Topic 4 (substance use challenges) constitute a closely linked semantic cluster. Although PTSD is a well-defined psychiatric disorder (disorder, 0.052), current research predominantly addresses its primary clinical challenge: comorbidity with substance use disorders (SUDs). The notably high weights of alcohol (0.145) and use (0.105), which surpass the foundational term PTSD (0.075), offer robust quantitative evidence that SUD-related comorbidity is a central focus in both academic research and clinical practice (A systematic review and meta-analysis of psychological interventions for comorbid post-traumatic stress disorder and substance use disorder, 2022, pp. 1051\u0026ndash;1061).\u003c/p\u003e \u003cp\u003eThe literature in this cluster addresses not only alcohol misuse but also the influence of specific substances such as cannabis (0.037) on the persistence of post-traumatic symptoms, often within the framework of the self-medication hypothesis. The inclusion of the keyword diagnostic (0.026) underscores ongoing scholarly emphasis on diagnostic precision and improvement. Although the DSM-5 four-factor structure\u0026mdash;intrusion, avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity\u0026mdash;remains foundational for clinical diagnosis, recent studies indicate that revisions may be necessary. Evidence supporting six- and seven-factor models suggests that the current structure may not adequately represent the complexity of PTSD symptomatology (A systematic literature review of PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM-5, 2016, pp. 302\u0026ndash;310; Petri et al., 2022, pp. 149\u0026ndash;159).\u003c/p\u003e \u003cp\u003eThe seven-factor Hybrid Anhedonia and Externalizing Behaviors model, which divides DSM-5 symptom clusters into intrusion, avoidance, negative affect, anhedonia, detachment, hyperarousal, and reckless or self-destructive behavior, has shown greater explanatory capacity for symptom heterogeneity across diverse populations (C et al., 2015, pp. 106\u0026ndash;113). The BERTopic clustering results align with this theoretical perspective, as alcohol, substance, and cannabis are prominently represented. It is important to note that PTSD is more closely linked to anhedonia than most substance use disorders, except for opioid use disorder; however, anhedonia does not occur uniformly across all SUD comorbidities (Stull et al., 2022). These findings indicate that BERTopic identifies substance-related comorbidity as a significant research focus and reveals varying relationships between problematic substance use and PTSD, suggesting that comorbidity may be influenced by different factors depending on the specific substance involved (Comorbidity of problematic substance use and other addictive behaviors and anxiety, depression, and post-traumatic stress disorder: a network analysis, 2024, pp. 1050\u0026ndash;1058).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Collective Trauma and Emergency Psychological Responses: Analysis of Topics 1, 2, and 3\u003c/h2\u003e \u003cp\u003eTopics 1, 2, and 3 collectively establish a structured research agenda addressing collective trauma and emergency psychological responses. Elevated c-TF-IDF weights for 'COVID' (0.084) and '19' (0.084) indicate that COVID-19 represents a primary traumatic stressor at the macro level (COVID-19 as a traumatic stressor is an indicator of mental health symptomatology, 2021, pp. 1\u0026ndash;5). These data demonstrate a rapid scholarly response to acute psychological stress reactions during the pandemic, as reflected by the weight for 'pandemic' (0.048) (Prevalence of posttraumatic and general psychological stress during COVID-19: A rapid review and meta-analysis, 2020).\u003c/p\u003e \u003cp\u003eExpanding upon this macro-level perspective, the analysis focuses on younger populations. Students (0.083/0.056) are identified as a primary data source (The prevalence of psychological stress in student populations during the COVID-19 epidemic: a systematic review and meta-analysis, 2022). This emphasis reveals a significant scholarly interest in anxiety (0.063) and depression (0.040) comorbidities within this vulnerable group (Status and epidemiological characteristics of depression and anxiety among Chinese university students in 2023, 2023). Topic 3 further refines the analysis by identifying sleep disturbances (0.045) as a key symptom in emergency stress responses (Cellini et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, pp. 300\u0026ndash;307). Collectively, these findings underscore the necessity for precise measurement and targeted interventions addressing both psychological and physiological symptoms in large-scale traumatic contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Etiological Mechanisms of PTSD (Topic \u0026minus;\u0026thinsp;1)\u003c/h2\u003e \u003cp\u003eAlthough Topic-1 is categorized as unassigned within the BERTopic framework, its core keywords delineate the theoretical frontier of PTSD research. These keywords primarily address disease susceptibility and etiological origins. The co-occurrence of genetic (0.060) and environmental (0.041) factors strongly supports the widespread adoption of gene\u0026ndash;environment interaction (G\u0026times;E) frameworks. Such frameworks elucidate individual differences in PTSD vulnerability and move beyond explanations based solely on trauma exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eAdditionally, alexithymia (0.037) has emerged as a transdiagnostic marker associated with disorder (0.043), highlighting growing research interest in emotion regulation deficits as a central mechanism in PTSD pathogenesis. This etiological emphasis provides a biopsychological foundation for refining treatment strategies and developing personalized intervention approaches.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study utilized BERTopic to systematically map the latent thematic structure and evolutionary patterns within post-traumatic stress disorder (PTSD) research. The resulting topic architecture confirms established findings in the literature and demonstrates a shift in contemporary scholarship toward emerging challenges and theoretical frontiers. Collectively, the findings indicate a transition in PTSD research from descriptive symptom documentation to integrative models that emphasize heterogeneity, comorbidity, and multilevel etiological mechanisms, thereby highlighting the field\u0026rsquo;s progression and current priorities.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Key Findings\u003c/h2\u003e \u003cp\u003eThe BERTopic results identified a prominent cluster of COVID-19\u0026ndash;related topics (Topics 1\u0026ndash;3), reflecting a rapid and concentrated scholarly response to large-scale collective stressors. The prominence and relative weight of these topics indicate that PTSD research during public health crises has prioritized vulnerable populations, particularly students, and has increasingly focused on emotional comorbidities such as anxiety and depression. Notably, sleep-related disturbances emerged as a quantifiable and recurrent symptom dimension, underscoring their value as early indicators for risk stratification and timely psychological intervention in emergency contexts.\u003c/p\u003e \u003cp\u003eA second major thematic axis highlights the persistent clinical challenge of PTSD comorbidity with substance use disorders (SUDs), as evidenced by the high salience of terms such as alcohol, use, and substance. Although the DSM-5 four-factor model remains the dominant diagnostic framework, the topic structure suggests that this model may not sufficiently capture clinically salient dimensions, particularly externalizing behaviors and anhedonia. In contrast, seven-factor or hybrid models appear better suited to explaining symptom heterogeneity across populations. The BERTopic clustering supports this perspective, as SUD-related themes converge with dimensions corresponding to externalizing behavior and anhedonia, thereby providing data-driven support for extended PTSD symptom models.\u003c/p\u003e \u003cp\u003eTopic-1 delineates the theoretical frontier of PTSD research by focusing on gene\u0026ndash;environment interactions (GxE) and alexithymia. The co-occurrence of genetic and environmental terms highlights the joint contribution of biological susceptibility and environmental stressors to PTSD vulnerability. Concurrently, alexithymia emerges as a transdiagnostic psychological marker, emphasizing deficits in emotional awareness and regulation. Rather than treating these mechanisms as independent, the findings suggest their conceptual integration: GxE processes provide a biological foundation of vulnerability, while alexithymia represents a psychological mechanism through which this vulnerability is expressed. Together, these factors offer a coherent explanation for the pronounced heterogeneity in PTSD symptom manifestation among individuals exposed to similar traumatic events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical and Clinical Implications\u003c/h2\u003e \u003cp\u003eThe bibliometric and topic modeling results provide empirical support for the growing consensus that multi-factor and hybrid PTSD models offer superior explanatory power compared to the traditional DSM-5 four-factor structure. By explicitly capturing dimensions such as externalizing behaviors and anhedonia, these models enhance conceptual clarity regarding symptom heterogeneity and comorbidity.\u003c/p\u003e \u003cp\u003eThe identification of youth populations and sleep disturbances as central themes in crisis-related PTSD research underscores their practical relevance as high-risk groups and measurable indicators during public health emergencies. These findings offer actionable guidance for rapid psychological screening, early intervention, and resource allocation.\u003c/p\u003e \u003cp\u003eAt the etiological level, the convergence of GxE mechanisms and alexithymia within the topic structure underscores their central role in shaping PTSD vulnerability and comorbidity patterns. This integrated perspective supports the development of precision-oriented risk stratification strategies and preventive interventions that address both biological susceptibility and emotional regulation deficits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eDespite its strengths, this study has several limitations. Although BERTopic effectively captures macro-level thematic structures and research trends within the Web of Science database, it does not permit causal inference at the individual level. Given these constraints, future research should extend the present findings in several directions.\u003c/p\u003e \u003cp\u003eFirst, to address these gaps, the clinical efficacy of integrated treatment approaches targeting complex comorbid profiles, particularly PTSD with SUDs and alexithymia, should be evaluated through randomized controlled trials (RCTs). Such studies are essential for translating thematic insights into evidence-based interventions.\u003c/p\u003e \u003cp\u003eSecond, longitudinal research designs are needed to clarify the temporal sequencing and mediating pathways underlying GxE mechanisms in PTSD development. Sustained investment in longitudinal datasets would enable a more precise examination of how genetic vulnerability and environmental stressors interact over time.\u003c/p\u003e \u003cp\u003eFinally, future studies should assess the cross-cultural and cross-contextual generalizability of PTSD symptom structures. Applying complementary methods such as network analysis to diverse trauma contexts, including childbirth-related PTSD or intensive care unit survivors, would further test the robustness and replicability of the identified symptom dimensions.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmour C, Mullerova J, Elhai (2015) J. 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This study employs bibliometric analysis of literature from the Web of Science (WoS) database, identifying 26 Highly Cited Papers as core references for detailed examination. The aim is to map the thematic structure and evolving research trends within PTSD scholarship. Application of the BERTopic model to construct topic distributions revealed three primary research foci. The first group, Topics 0, 1, and 4, addresses emergency psychological responses during the COVID-19 pandemic (COVID-19, 0.084), with a particular focus on prevalence rates and mental health outcomes among student populations. The second, Topic 2, examines diagnostic and therapeutic strategies, emphasizing the interactive effects of genetic factors (genetic, 0.060), environmental influences (environmental, 0.041), and alexithymia (0.037). The third, Topic 3, investigates alcohol use (0.145) and substance use, highlighting the comorbidity challenges between PTSD and substance use disorders (SUDs). These findings indicate that future research and clinical practice should prioritize risk stratification and integrative treatment strategies based on gene–environment interaction (G×E) models to more effectively address the complex demands of the post-pandemic era.\u003c/p\u003e","manuscriptTitle":"Emerging Research Trends and Thematic Structure of Post-Pandemic Post-Traumatic Stress Disorder (PTSD) \nA Bibliometric and BERTopic Based Topic Modeling Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 09:01:18","doi":"10.21203/rs.3.rs-8497724/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"157194e2-bbe7-44c2-9d1f-08f85912e1f8","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60580629,"name":"Psychology"},{"id":60580630,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-01-05T09:01:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 09:01:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8497724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8497724","identity":"rs-8497724","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}