Network Analysis Model Research Articles

Harnessing AI to Illuminate the Soul of the Organization

Artificial intelligence and advanced analytics are emerging as promising new tools for gaining meaningful insights into organizational culture. Traditional qualitative methods for assessing culture are time-intensive and limited in scope, while AI offers data-driven, comprehensive approaches. This article reviews recent developments in using AI at both the macro and micro levels to enhance understanding of cultural dynamics. At a macro level, AI is powering new tools for conducting large-scale cultural assessments across multiple dimensions. More granularly, natural language processing, network analysis, and predictive modeling are illuminating sentiments, interactions, and influence within internal communications data. Examples from pioneering organizations demonstrate impactful applications of cultural insights derived from AI in areas like mergers and acquisitions, recruiting and onboarding, diversity initiatives, and efforts to nurture vibrant cultures. While the field is still evolving, innovative practitioners are beginning to harness AI’s potential to illuminate the complexity of organizational culture in compelling new ways.

Structural characteristics and mechanism of collaborative environmental governance network of urban agglomerations: perspective of multilevel network

Collaborative environmental governance (CEG) is increasingly advocated to address the environmental risk issues in the integrated development of urban agglomerations. Constructing an effective CEG network from the perspective of interdependent multilevel network plays a vital role in promoting the environmental governance of urban agglomerations. To investigate the structure characteristics and formation mechanism of CEG network, this paper takes the Yangtze River Delta urban agglomeration as the research area, and employes the social network analysis and Exponential Random Graph Model (ERGM) methods to analyze the CEG network, which consists of the collaborative network of cities, relationship network of topics, and affiliation network connecting cities to topics. Research results show that the CEG level in the Yangtze River Delta urban agglomeration continues to improve, while the CEG network is still not in a tightly connected state. For the collaborative network of cities, it presents the small world characteristics and forms a cooperative trend of “central-subcentral-peripheral city.“For the relationship network of topics, the evolution of environmental governance topics is characterized by “from aspect to point.” For the affiliation network connecting cities to topics, as the diversity of environmental governance topics increases among cities, cities within the Yangtze River Delta urban agglomeration tend to share the similar topics. In addition, the interactive triangular structures, star structures, open triangular structures and closed triangular structures in the network can promote the formation of new cooperative relationships in CEG network.

The relationship between interschool social networks and student achievement

ABSTRACT Various forms of networks in education have been proposed by scholars, policymakers, and educational administrators as a strategy for school improvement. However, there are few empirical studies on the effect of interschool networks. We analyzed data from 76 elementary schools in a Midwestern state to examine the association between interschool relationships and student achievement by employing social network analysis and hierarchical linear modeling (HLM). After controlling for school and district demographic characteristics, we found that (a) schools that reported to have a stronger relationship with other schools had better math achievement, and more growth in reading achievement; (b) schools with reciprocal relationships had better math and reading achievement, and more growth in reading achievement; and (c) schools that connected to more influential schools in the network had better math and reading achievement, and more growth in reading achievement. We discussed the implications for research, policy, and practice.

Correlation between benefit finding and caregiving abilities among family caregivers of patients with lung cancer: a network analysis.

This study aimed to construct a network analysis model for benefit finding (BF) and caregiving abilities to clarify the interrelationships and mutual influences among different dimensions and propose nursing strategies. Convenience sampling was used to select 272 family caregivers of patients with lung cancer admitted to a tertiary hospital in Guangzhou City, China, from April 2023 to November 2023. Socio-demographic and disease characteristics questionnaire, the revised BF Scale, and the Family Caregiver Task Inventory (FCTI) were used for data collection. The R software was used to construct networks for BF and caregiving abilities, and network analysis methods were employed to identify network characteristics, core nodes, and bridge nodes. In the overall network, the social relationship dimension (rs = 3.04) exhibited the highest strength centrality index, followed by the family relationship dimension (rs = 2.94). In addition, the social relationship dimension (rbs = 0.30) had the highest bridge strength centrality index, followed by the dimension of addressing personal emotional needs (rbs = 0.26). This study provides a new perspective on exploring the underlying mechanisms of interaction among different dimensions of BF and caregiving abilities in family caregivers of patients with lung cancer using network analysis. The findings suggest that healthcare professionals can improve family caregivers' social relationships, family relationships and address emotion regulation to enhance BF and caregiving abilities. Specific nursing strategies are proposed, offering new intervention targets for enhancing BF and caregiving abilities among family caregivers of patients with lung cancer.

Abstract 4137713: Epsin-Mediated PCSK9-LDLR Interaction: A New Therapeutic Target for Atherosclerosis

Introduction: Atherosclerosis, a leading cause of cardiovascular diseases, causes about 17.9 million deaths annually. It is driven by high levels of LDL cholesterol (LDL-C). The liver's LDL receptor (LDLR) removes LDL-C from the bloodstream, but its degradation by PCSK9 exacerbates the condition. Epsin, an endocytic adaptor protein crucial for clathrin-mediated endocytosis, plays a significant role in atherosclerosis, particularly lipid metabolism. Hypothesis: We propose that hepatic epsins significantly contribute to atherosclerosis by facilitating the PCSK9-induced degradation of LDLR, thus impeding LDL-C clearance. Objectives: This study aims to investigate hepatic epsins' role in PCSK9-mediated LDLR degradation and its effects on LDL-C levels and atherosclerosis development. Using bioinformatics, we will analyze enriched pathways and networks to study epsins modulation, which will be validated through biochemical assays and in vivo experiments. Methods: Liver-specific double knockout (DKO) mice lacking epsin1 and epsin2 were created on an ApoE -/- background. Atherosclerosis was induced using a Western diet (WD), and blood cholesterol and triglyceride levels were monitored. We employed high-resolution single-cell RNA sequencing (scRNA-seq) to analyze cellular transitions, intercellular interactions, and Gene Ontology pathway enrichment within hepatocyte-derived data. Results: RNA velocity reveals the transition from lipogenic Alb hi Hepatocytes to glucogenic Hnf4a hi Hepatocytes in Liver-DKO mice on an ApoE -/- background on a WD. Liver-DKO mice on an ApoE -/- background on a WD exhibited significantly reduced atherogenesis and lower blood cholesterol and triglyceride levels compared with wild-type on an ApoE -/- background on a WD. Gene Ontology enrichment analysis showed elevated pathways for LDL particle clearance and enhanced LDLR-cholesterol communication scores in Liver-DKO mice, suggesting improved LDL-C clearance. Nanoparticle-encapsulated siRNAs targeting liver epsins effectively inhibited atherosclerosis. Conclusion: Epsin depletion in the liver upregulated LDLR protein levels, and PCSK9-triggered LDLR degradation was abolished, preventing atheroma progression. Targeting liver epsins presents a novel therapeutic strategy for atherosclerosis, supported by network analysis and predictive modeling for effective interventions.

A Simple Neural Network for Estimating Fine Sediment Sources Using XRF and XRD

Suspended sediment (SS) has a wide range of negative effects such as increased water turbidity, altered habitat structures, sedimentation, and effects on hydraulic systems and environmental engineering projects. Nevertheless, the methods for accurately determining SS sources on a basin-scale are poorly understood. Herein, we used a simplified neural network analysis (NNA) model to identify the sources of SS in Japan’s Oromushi River Catchment Basin. Fine soil samples were collected from different locations of the catchment basin, processed, and separately analysed using X-ray fluorescence (XRF) and X-ray diffraction (XRD). The sampling stations were grouped according to the type of soil cover, vegetation type and land-use pattern. The geochemical components of each group were fed into the same neural network layer, and a series of equations were applied to estimate the sediment contribution from each group to the downstream side of the river. Samples from the same sampling locations were also analysed by XRD, and the obtained peak intensity values were used as the input in the NNA model. SS mainly originated from agricultural fields, with regions where the ground is covered with volcanic ash identified as the key sources through XRF and XRD analysis, respectively. Therefore, based on the nature of the surface soil cover and the land use pattern in the catchment basin, NNA was found to be a reliable data analytical technique. Moreover, XRD analysis does not incorporate carbon, and also provides detailed information on crystalline phases. The results obtained in this study, therefore, do not depend on seasonal uncertainty due to organic matter.

텍스트 마이닝을 활용한 이주배경 구성원의 문화적응 스트레스의 연구동향 분석

This study aims to explore research trends in acculturation stress using text mining techniques. To achieve this, 387 papers related to acculturation stress, published in KCI-indexed journals over 16 years since the enactment of the Multicultural Families Support Act in 2008, were selected. Based on the Korean abstracts, a total of 1,762 keywords were extracted. NetMiner 4.5 was utilized to analyze unstructured data, employing keyword network analysis and LDA-based topic modeling. The study analyzed keyword frequency, co-occurrence keyword pairs, and centrality. Through LDA-based topic modeling, it was found that five topics were the most appropriate. Based on the derived results, the study reviewed research directions to advance the academic development of acculturation stress and provided educational implications.

Transcriptome Sequencing-Based Screening of Key Melatonin-Related Genes in Ischemic Stroke.

Ischemic stroke (IS) is a complex syndrome of neurological deficits due to stenosis or occlusion of the carotid and vertebral arteries for which there is still no effective treatment. Melatonin, a hormone secreted by the pineal gland, has multiple biological effects, such as antioxidant and anti-inflammatory properties, circadian rhythm regulation, and tissue regeneration, demonstrating potential applications in the treatment of IS. The aim of this study was to investigate key melatonin-regulated genes associated with IS using transcriptome sequencing and bioinformatics analyses and to explore their potential mechanisms of action in the disease process. We obtained gene expression data related to ischemic stroke (IS) from the Gene Expression Omnibus (GEO) database and identified candidate genes using machine learning algorithms. We then assessed the predictive power of these genes using PPI network analysis and diagnostic models. Finally, a series of enrichment analyses identified four key genes: ADM, PTGS2, MMP9, and VCAN. In addition, we determined the mRNA levels of these four key genes in an IS rat model using qPCR and found that all of these genes were significantly upregulated in the IS model compared to the control group, which is consistent with the results of previous analyses. Meanwhile, these genes have biological functions such as regulating vascular tone, participating in the inflammatory response, influencing tissue remodeling, and regulating cell adhesion and proliferation, playing key roles in the pathogenesis of IS. Therefore, we suggest that these four key genes may serve as prospective biomarkers for IS and help predict the risk of developing IS. In conclusion, this study elucidates for the first time the potential role of melatonin in the pathogenesis of IS and lays the foundation for in-depth studies on the functions of these key genes in the pathophysiology of IS and their potential applications in clinical diagnosis and treatment.

Key factors influencing sustainable population growth: A DEMATEL-ANP combined approach

This study highlights the importance of sustainable social demographic growth. It uses a model that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Network Process (ANP) to examine key factors affecting this growth and their interactions. The analysis focuses on six critical factors: economic development, education and gender equality, health services, environmental sustainability, immigration policies, and technological advancement. Experiments using government and international organization databases include comparative experiments with deep learning prediction models, ensemble learning models, Causal Inference Models, complex network analysis models, and agent-based models. Comparison metrics cover accuracy, precision, recall, and F1 score. The results indicate that, with a data volume of 4000, the optimized model achieves an accuracy of 0.973, precision of 0.981, recall of 0.969, and an F1 score of 0.89, demonstrating the model's superior performance. The DEMATEL method analyzes the direct relationships among the factors. The results show that economic development and technological advancement have impact scores of 3.91 and 3.43, respectively, indicating their strong influence on other factors and their role in promoting sustainable demographic growth. Education and gender equality, health services, and technological advancement each have impact scores of 3.39, meaning they are significantly affected by other factors and are sensitive in the growth process. Finally, the ANP method is used to calculate the weights of each factor, determining their relative importance in sustainable social demographic growth. The results highlight that economic development level and technological advancement and innovation are core factors influencing sustainable social demographic growth, with significant direct and indirect impacts on other factors and a crucial role in the overall system. These findings provide a scientific basis for formulating relevant policies and interventions, particularly in prioritizing and strengthening economic and technological development strategies. This study offers valuable insights for research in demography, sustainable development, and social policy formulation.

뉴스 기사 빅데이터 분석을 통한 북한이탈주민의 사회적 이슈와 언어 문제 탐색: 2019-2024

Objectives The purpose of this study is to explore social issues and language problems that appear in news articles related to North Korean defectors and to suggest their implications. Methods 561 news articles related to North Korean defectors covered by major domestic newspapers and media companies from 2019 to June 2024 were collected. For analysis tools, language network analysis and topic modeling techniques were used to analyze major keywords and topics and visualize them. In addition, based on the analysis results, social issues and language problems of North Korean defectors were explored, and Korean language education implications were presented. Results he research results are as follows. First, the amount of news coverage has been increasing significantly over the past three years, and political articles have shown an overwhelmingly high proportion. Second, the main keywords are ‘government, support, human rights, protection, and settlement’. The main topics were protection of human rights of North Korean defectors, increasing number of North Korean defectors among elites and the 2030 generation, and complaints of difficulties due to mental and economic discrimination and prejudice in the process of settling in Korea. The content of the government's integrated support policy accounted for a high proportion. Third, it was found that North Korean defectors were struggling with the language difference between North and South Korea, and Korean language education focusing on pronunciation, accent, and vocabulary in each area was necessary for them. Conclusions The main topic of the news article was the integrated support for the protection of human rights and stable settlement of North Korean defectors. However, although North Korean defectors complain of ‘difficulty’ due to economic, psychological, emotional, and linguistic differences, these related topics are hardly covered in the media. Therefore, this paper suggested implications for Korean language education research for North Korean defectors' language adaptation.

Crisis management from a relational perspective: an analysis of interorganizational transboundary crisis networks

Abstract Although transboundary crises have gained relevance in an increasingly interdependent world, our understanding of the relational dynamics governing these phenomena remains limited. This paper addresses this knowledge gap by identifying common characteristics across interorganizational transboundary crisis networks and drivers of tie formation in successful structures. For this purpose, it applies descriptive Social Network Analysis and Exponential Random Graph Models to an original dataset of three networks. Results show that these structures combine elements of issue networks and policy communities. Common features include moderately high centralization, reciprocated ties, core-periphery structures, and the popularity of international organizations. Additionally, successful networks display smooth communication between NGOs and international organizations, whereas unsuccessful networks have fewer heterophilous interactions. Transitivity seems to play a role in network success too. These findings suggest that crisis networks are robust structures that reconcile bridging and bonding dynamics, thereby highlighting how evidence from relational studies could guide transboundary crisis management.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

Relationships between mental toughness, eustress-distress, and mindfulness in adolescents: A network analysis and mediator model testing.

The main aim of this study was to investigate adolescents' eustress-distress and mindfulness by examining mental toughness (MT) using network and mediator analyses. The study included 414 adolescents. The results showed that MT was positively related to eustress and mindfulness, but distress was negatively related to MT. Based on the network analysis findings, we tested mindfulness' mediating role in the relationship between eustress-distress and MT. The findings showed that mindfulness played a significant mediating role. However, the mediating role of mindfulness was negative for the relationship between distress and toughness and positive for eustress. These findings advance eustress, distress, and mindfulness as mechanisms for understanding the effects of MT. Considering current knowledge of MT, eustress, distress, and mindfulness, the results are discussed.

글 없는 그림책 연구 동향 분석 : 키워드 네트워크 분석 및 토픽모델링을 중심으로

This study explores research trends in wordless picture books using text mining techniques. For this purpose, abstracts of 181 articles were collected, and then data preprocessing and analysis were performed. In the data analysis process, keyword network analysis and topic modeling analysis were performed. The keyword analysis showed that the most frequent word was ''young children'', followed by ''storytelling'' and ''wordless picture book activities''. The network analysis showed that the strength of the connections between ''wordless picture book activities → young children'' and ''using wordless picture books → young children'' was high. This suggests that in studies of wordless picture books, the use of picture book activities or wordless picture books is mainly targeted at young children. Topic modeling resulted in four themes: multicultural approach and visual literacy, playfulness and artistic development, integration of language and art, and methodology of integration and repetition. This study is significant in that it identifies major research themes in the field of wordless picture books and suggests future research directions.

Centrality and bridge connections between cognitive emotion regulation strategies and professional identity among Chinese undergraduate nursing students: A network analysis

AimTo construct a network structure for cognitive emotion regulation among Chinese undergraduate nursing students and identify central nodes and to explore the bridge connections between cognitive emotion regulation strategies and professional identity from the perspective of network analysis. BackgroundUndergraduate nursing students are currently in a poor psychological condition and cognitive emotion regulation strategies can help them use positive approaches to regulate their emotions. There may be a link between cognitive emotion regulation strategies and professional identity. DesignThis was a cross-sectional study that used network analysis. MethodA total of 218 Chinese undergraduate nursing students were selected and surveyed using the Cognitive Emotion Regulation Questionnaire and the Professional Identity Questionnaire. A network analysis model was constructed and the related indices were calculated using R 4.3.0 software. ResultsNetwork analysis showed that the central nodes of undergraduate nursing students’ cognitive emotion regulation strategies were positive reappraisal, refocusing on planning and catastrophising; In the bridge network of cognitive emotion strategies and professional identity, professional self-concept, positive reappraisal, benefits of stay and risk of resignation and refocusing on planning were the nodes with the strongest bridge strength. ConclusionThe salient central and bridge nodes can serve as potential targets for interventions aimed at improving the mental health of undergraduate nursing students. Nursing educators must be trained in cognitive emotion regulation and appropriately guided to use positive emotion regulation strategies in their studies and work. Educators should focus on the relation between cognitive emotion regulation and professional identity to improve the mental health of nursing students and stabilise the nursing workforce.

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

A conceptual framework for exploring the use of a green marketing mix and brand image to attract customers for green services while visiting a hotel: A step toward marketing sustainability

Recently, the application of green marketing strategies to promote marketing sustainability has increased in response to the growing demand for eco-friendly products, Price, promotion, place, and service are all variables that influence purchasing decisions, and the current study aims to confirm this. as the brand plays a significant role as well because it is regarded as the driving force behind consumer attraction in society. For this study, a framework connecting brand image (brand benefits and competence, brand identity, brand personality, brand association, brand attitude, and behavior), the green marketing mix (green services, green place, green price, green promotion), and green purchasing decisions were created to better understand the relationships among these variables. Artificial neural network (ANN) analysis and structural equation modeling (SEM) based on deep learning will be conducted via the new hybrid analysis approach. The research employed a survey of hotel customers in Iraq, Additionally, the researchers conducted structured interviews with a group of hotel managers. The issue of green marketing and how it relates to environmentally conscious consumer choices has received much attention, which has made addressing sustainability a top priority in the hospitality industry, particularly in hotels. In this study, customers were the main focus since their choice is crucial regarding switching to green services. However, a group of these hotel managers were interviewed to validate the findings and establish credibility. The current study was limited since it focused on clients in general, without considering clients from outside Iraq. Future studies can examine the variable of organizational support and the degree of change acceptance among customers.

Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling

Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling

Targeting ADA and HIV-1 Nef Protein with Protease Inhibitors: A Repurposing Strategy through Molecular Networking and an In silico Approach for Integrated Management of HIV Co-infected with Abdominal Tuberculosis

Background: Background: The co-infection of HIV and abdominal TB poses a worldwide danger to humanity. This is because there are more strains of bacteria that are resistant to many classes of currently available medications. According to current findings, repurposing existing available medications will result in more effective functioning than using newly designed medications. Objective: Based on this fact, we hypothesised that the PI could be repurposed; we used Food and Drug Administration (FDA)-approved PI drugs to treat HIV co-infected patients with abdominal TB, and a computational study has been conducted. Methods: This comprises network analysis models to find their protein drug interaction (PDI) through a search tool for interacting chemicals (STICH) module of Cytoscape network analysis model followed by the screening of these drugs for their ADMET prediction and binding affinity with adenosine deaminase (ADA), a protein responsible for abdominal TB, and the HIV-1 Nef protein, responsible for the regulation of immune function (CD4+). Results: The network analysis showed 13 nearest binding drugs of these proteins of interest. The ADMET study result showed the pharmacologically relevant parameters that have a significant effect on the binding affinity, bioavailability, and toxicity of PI. The top three scores achieved by PI against adenosine deaminase enzyme activity (PDB ID: 1A4M) are viz., -23.7919, -23.3529, and -22.6773 for Ritonavir, Tipranavir, and Atazanavir, respectively. The top three scores achieved by PI against HIV-1 Nef protein activity (PDB ID: 6URI) are viz., -28.7321, -28.4987, and -28.3155 for Atazanavir, Tipranavir, and Simeprevir, respectively. The active site of ADA and HIV-1 Nef proteins comprises amino acid residues such as for Tipranavir: Arene-Cation interaction (Phenyl and Pyridine)- Arg B1081, and Lys B1033 (1A4M) and Arene-Cation interaction (Pyridine and Phenyl)- Lys D11 and Arg D33; Sidechain acceptor Thr B40; Sidechain donor- Asp D30; Backbone donor- Ala B37 (6URI). Atazanavir: Arene-Cation interaction (Phenyl)- Lys A254 and Lys B1033; Sidechain acceptor - Arg A251 (1A4M). Conclusion: Thus, from the computational studies carried out, we could obtain hints for optimising the molecular selectivity of the PI to provide help in the design of new compounds via the repurposing strategy of the FDA-approved PI for effective treatment of co-morbidity with HIV and abdominal TB. However, further pharmacokinetics, pharmacodynamics, preclinical, and clinical studies permit the design of the new agents without undesirable interactions.

Understanding the rationales and information environments for early, late, and nonadopters of the COVID-19 vaccine

Anti-vaccine sentiment during the COVID-19 pandemic grew at an alarming rate, leaving much to understand about the relationship between people’s vaccination status and the information they were exposed to. This study investigated the relationship between vaccine behavior, decision rationales, and information exposure on social media over time. Using a cohort study that consisted of a nationally representative survey of American adults, three subpopulations (early adopters, late adopters, and nonadopters) were analyzed through a combination of statistical analysis, network analysis, and semi-supervised topic modeling. The main reasons Americans reported choosing to get vaccinated were safety and health. However, work requirements and travel were more important for late adopters than early adopters (95% CI on OR of [0.121, 0.453]). While late adopters’ and nonadopters’ primary reason for not getting vaccinated was it being too early, late adopters also mentioned safety issues more often and nonadopters mentioned government distrust (95% CI on OR of [0.125, 0.763]). Among those who shared Twitter/X accounts, early adopters and nonadopters followed a larger fraction of highly partisan political accounts compared to late adopters, and late adopters were exposed to more neutral and pro-vaccine messaging than nonadopters. Together, these findings suggest that the decision-making process and the information environments of these subpopulations have notable differences, and any online vaccination campaigns need to consider these differences when attempting to provide accurate vaccine information to all three subpopulations.