Social Network Analysis Research Articles

The unfolding of geopolitical tensions on social networks: a social network analysis of Twitter and Reddit conversations

PurposeSocial networks can not only mobilize individuals for collective action but also pose risks, potentially leading to political challenges and societal unrest. Information consumption varies across platforms, with platform characteristics influencing user interactions and information sharing; yet this has received limited attention in scholarly literature. Acknowledging platform-specific differences, this paper seeks to enhance our understanding of the mechanisms driving information diffusion on social networks in the context of geopolitical tensions.Design/methodology/approachThe structural communication features on Twitter and Reddit are explored using schema theory and the concept of social media platform schema. Comparisons are drawn with social network analysis and content analysis of communication dynamics surrounding geopolitical tensions in India–Qatar relations, followed by the context of geopolitical tensions between India and Pakistan.FindingsThe results illustrate how content-based connections on Reddit foster closer ties within subreddits but less connectivity between them, contrasting with Twitter’s profile-based connections. These distinct characteristics lead to varied information diffusion patterns and shape the diversity of opinions, influencing community structures and affecting the emotional tenor of discourse.Originality/valueSocial networks can potentially influence geopolitical events, but focusing on one platform overlooks differences in how information spreads and the influence each platform holds. Recognizing this, our comparative analysis of social networks’ structural attributes highlights their crucial roles in shaping user engagement and information diffusion. It lends theoretical support to the notion of social media platform schema with empirical insights into how users’ perceptions of these schemas impact thematic and emotional differences in platform discourse related to geopolitical tensions.

Chemical signals and social structures strengthen sexual isolation in Drosophila pseudoobscura

Species that coexist in hybrid zones sexually isolate through reproductive character displacement, a mechanism that favours divergence between species. In Drosophila, behavioural and physiological traits discourage heterospecific mating between species. Recently, social network analysis revealed flies produce strain-specific and species-specific social structures. A gene, degrees of kevin bacon (dokb) has also been discovered that accounts for differences in social structures between flies. Why differences in social structures exist between drosophilids is currently unknown. Here we show through an experimental evolution study that six generations of selection in experimental sympatry led to the divergence of social structures measured in Drosophila pseudoobscura and Drosophila persimilis flies. We found that the frequency of hybrid offspring decreased within a few generations, suggesting social structures are associated with the sexual isolation of species. We also report increased species’ differences in the concentration of the cuticular hydrocarbon 5, 9-pentacosadiene after six generations of selection. The mean concentration of this compound converged in female flies of both species and diverged in male flies of both species, suggesting a quantitative link between increased sexual dimorphism and sexual isolation. Our results suggest that chemical signals, together with social structures, increase the sexual isolation between species in hybrid zones.

‘It's about collaboration’: a whole-systems approach to understanding and promoting movement in Suffolk

BackgroundPopulation-levels of physical activity have remained stagnant for years. Previous approaches to modify behaviour have broadly neglected the importance of whole-systems approaches. Our research aimed to (i) understand, (ii) map, (iii) identify the leverage points, and (iv) develop solutions surrounding participation in physical activity across an English rural county.MethodsA systems-consortium of partners from regional and local government, charities, providers, deliverers, advocacy groups, and health and social care, and public health engaged in our research, which consisted of two-phases. Within Phase 1, we used secondary data, insight-work, a narrative review, participatory workshops, and interviews in a pluralistic style to map the system-representing physical activity. Phase 2 began with an initial analysis using markers from social network analysis and the Action Scales Model. This analysis informed a participatory workshop, to identify leverage points, and develop solutions for change within the county.ResultsThe systems-map is constructed from biological, financial, and psychological individual factors, interpersonal factors, systems partners, built, natural and social environmental factors, and policy and structural factors. Our initial analysis found 13 leverage points to review within our participatory workshop. When appraised by the group, (i) local governing policies, (ii) shared policies, strategies, vision, and working relationships, (iii) shared facilities (school, sport, community, recreation), and (iv) funding were deemed most important to change. Within group discussions, participants stressed the importance and challenges associated with shared working relationships, a collective vision, and strategy, the role of funding, and management of resources. Actions to leverage change included raising awareness with partners beyond the system, sharing policies, resources, insight, evidence, and capacity, and collaborating to co-produce a collective vision and strategy.ConclusionsOur findings highlight the importance and provide insight into the early phase of a whole-systems approach to promoting physical activity. Our whole-systems approach within Suffolk needs to consider methods to (i) grow and maintain the systems-consortium, (ii) create a sustainable means to map the system and identify leverage points within it, and (iii) monitor and evaluate change.

Identifying the Impacts of Social Movement Mobilization on YouTube: Social Network Analysis

This study explores the potential of social media in improving education, engagement, and mobilization for climate change initiatives. Using the theoretical framework of resource mobilization and methods such as social network analysis (SNA) and bipartite networks, it examines how effective deployment of resources such as information, social capital, and organizational capabilities can help in the progression of collective movements. Social media platforms, particularly YouTube, significantly influences network structures by facilitating resource mobilization and driving essential engagement. This study extracted data from NodeXL and found that YouTube is an effective medium in disseminating climate change information and delivering educational content to a multilingual audience. Additionally, video affordances such as storytelling, audio–visual effects, and concise narratives enhance viewer interest and engagement, increasing resource mobilization effectiveness. This research offers insights into optimizing social media use for effective resource mobilization and engagement in climate change initiatives.

Analysis of Social Network Dynamics Around the Hashtag #ESG ON X (FORMERLY TWITTER): Evolution, Influencers and Topics

Objective: To investigate the dynamics of the social network formed around the hashtag #ESG on X (formerly Twitter), with the aim of analyzing its evolution over time, identifying the main topics of debate, actors, influencers and communities, and exploring the geographic origin of the main participants, observing regional or global patterns of participation. Theoretical Framework: This topic highlights the concepts of ESG and its evolution, the relevance of social media in the dissemination of the ESG agenda and the findings of previous studies on ESG and social media. Method: Social Network Analysis (SNA) techniques were adopted, with the aid of NodeXL software. Data collection was carried out by extracting public posts on X related to the hashtag #ESG between January 1 and December 31, 2023. Results and Discussion: The results revealed a complex and heterogeneous network, with different levels of influence and connectivity among users. Key figures who play central roles in the dissemination of information about ESG were identified. In the discussion section, these results are contextualized in light of the theoretical framework. Research Implications: By mapping key actors and communities, companies can identify their main stakeholders and direct their communication and engagement actions, developing more effective strategies to promote their practices and connect with their audiences. Originality/Value: This study contributes to the literature by exploring the dynamics of ESG discussions on Twitter over the course of an entire year, offering a continuous rather than a one-off analysis. The relevance and value of the research are evidenced by the identification of seasonality, influencers and key topics, with potential impact on understanding interactions and influences on social media about ESG.

Application of Graph Theory in Social Network Analysis

This literature review aims to discuss the application of graph theory in analyzing social and information networks. We first introduce some key network properties, such as clustering coefficient (transitivity), centrality, and diameter, which are crucial for understanding the dynamics of information dissemination within networks. Then, we talk about, based on these properties, how graph theory can be utilized to analyze social and information networks. Lastly, we provide an overview of various fundamental social and information models including the SIR model and the Linear Threshold model. For the SIR model, we go over the definition of the SIR model, explore the mathematical methods applied to analyze the SIR model, provide one example of how the SIR model reflects the nodes status in the network, and discuss the application of the SIR model in Covid-19 epidemic. For the Linear Threshold model, we review its basic properties and explain how it can be used to calculate the maximum influence in a network

The Formation of a Language of Science: Vitalism in 18th-century Scotland

Vitalism was a scientific theory that prevailed in 18th-century Scotland. Due to the influence of Boerhaave and the teaching of native scholars, vitalism appeared in Scotland in the first half of the 18th century. Its prosperity was closely related to the personal activities of William Cullen and marked by the acceptance and use of vitalism by other new disciplines, especially chemistry. Due to the continuous progress of experiments and the update of experimental instruments, vitalism was gradually replaced by experimental conclusions. Analyzed by social network analysis, the influencing factors affecting the spread of vitalism in Scotland in the 18th century are obtained. Officially, the University of Edinburgh and the teacher-student relationship were the main influencing factors; unofficially, William Cullens influence was the main influencing factor. As a scientific theory, vitalism experienced a limited rise and fall process; as a language of science, vitalism was borrowed by literature and other disciplines and has an influence even in biology today.

When engagement meets departure: how social capital, social engagement and social support impact churn within online health communities

PurposeOnline health communities can be both supportive and vulnerable and highly rely on stable engagement from core members. Understanding the factors affecting core member churn is vital. This study examines a hair-loss-related online health community, taking a meso-level community approach. It investigates how users’ potential social capital, proactive social engagement and received social support in the community influence their churn.Design/methodology/approachThis study collected the complete dataset spanning 13 years from a hair-loss-related online health community in mainland China. It identified 4,270 core users with over ten posted messages each, along with their associated text. Social network analysis and survival analysis were then used to calculate user node centrality and create a hazard model for user churn.FindingsThe findings indicate that increased social capital and social support can help prevent churn. However, higher social engagement alone does not directly reduce users’ likelihood to withdraw. Instead, users are more likely to stay in the community when highly engaged if they also receive substantial social support. Social support emerges as a critical factor in this mechanism.Originality/valueThis study innovatively adopts a meso-level community perspective, investigating user churn within an online health community for individuals with a self-limiting condition – hair loss. The research probes the interactive effects of user engagement and social support on churn within the online health community. Advice on sustaining member participation is given based on the findings as well as Chinese culture.

Mapping the community school policy network using Twitter data and social network analysis

This research sought to understand how organizations have adapted to the evolving policy ecosystem to scale and sustain a justice-orientated approach to education reform. Using descriptive social network analysis to map the policy information network that formed around Community Schools, the authors identified the influential organizations in the network and described key characteristics of its structure. They drew from Twitter data and related media articles over a five-year period to categorize the actors engaged in the Community School information network and map its geographic distribution across the United States. The findings depict a broad, multi-state network focused on promoting a proactive, positive policy agenda that differed substantially from those described in extant studies of resource networks that advance neoliberal education reforms. The authors close by discussing Twitter as a policy platform and how community-based coalitions can influence education politics and policy.

The Roots of Business Familism in Southern Italy. A Quantitative Analysis of the 19th Century

Abstract This paper investigates the historical roots of business familism in Southern Italy. Using a social network analysis and employing surnames as proxies for family ties, we introduce and explore various measures of business familism. Our analysis reveals that the persistent embeddedness of firms in family ties was a structural feature of the business system in the South throughout the 19th century. The analysis also documents significant variations in the trends of business familism across different economic sectors.

Where power and scholarship collide: Gender and coauthorship in public administration research

AbstractPublishing is a source of capital and power in academia, and coauthoring is a common way to publish. However, studies in public administration have not yet examined the structure of coauthorship patterns, how these patterns have evolved over time, or the extent to which these patterns are gendered. We use bibliometric data to examine coauthorship in public administration scholarship over four decades with a particular focus on gendered patterns. Descriptive statistics, regression, and social network analysis suggest that when women are first authors, the research team is more likely to contain other women and while women are increasingly represented in coauthorship structures, men‐only groups of coauthors continue to persist. These findings have implications for the coauthoring practices of individual scholars, perceptions of coauthorship in hiring, tenure, and promotion decisions, and efforts in the field to promote diversity, equity, and inclusion.

Graph Neural Networks for Enhanced Social Network Analysis

Social network analysis (SNA) is an important approach for understanding complex linkages and interactions between entities. Traditional approaches frequently fail to capture the complexities of network data due to its non-Euclidean character. Graph Neural Networks (GNNs) offer an innovative approach to data analysis by modelling node, edge, and graph features using graph structures and neural network topologies. This study investigates the use of GNNs in social network analysis, focusing on problems such as community recognition, impact maximization, link prediction, and sentiment analysis. Our analysis of cutting-edge GNN models shows how they effectively capture and utilize topological and contextual information from social networks

Integrating techniques of social network analysis and word embedding for word sense disambiguation

PurposeThis research addresses the challenge of polysemous words in word embedding techniques, which are commonly used in text mining. It aims to resolve word sense ambiguity by introducing a social network sense disambiguation (SNSD) model based on social network analysis (SNA).Design/methodology/approachThe SNSD model treats words as members of a social network and their co-occurrence relationships as interactions. By analyzing these interactions, the model identifies words with high betweenness centrality, which may act as bridges between different word sense communities, indicating polysemy. This unsupervised method does not rely on pre-tagged resources and is validated using the IMDb dataset.FindingsThe SNSD model effectively resolves word sense ambiguity in word embeddings, proving to be a cost-effective and adaptable solution to this issue. The experimental results demonstrate that the model enhances the accuracy of word embeddings by accurately identifying the correct meanings of polysemous words.Originality/valueThis study is the first to apply SNA to word sense disambiguation (WSD). The SNSD model offers a novel, unsupervised approach that overcomes the limitations of traditional supervised or knowledge-based methods, providing a valuable contribution to the field of text mining.

The Role of Learning Analytics in Evaluating Course Effectiveness

This study aims to examine the use of learning analytics in course evaluation within higher education institutions, in order to identify effective methodologies and best practices for leveraging data to improve educational effectiveness. Following the PRISMA guidelines, a systematic literature search was conducted in Scopus, yielding 34 relevant studies published between 2015 and 2024 for analysis. The results reveal six key categories of learning analytics applications: sentiment analysis, questionnaire analysis, engagement analysis, topic classification, predictive modelling, and performance analysis. The data sources for learning analytics applications primarily include questionnaires and learning management systems. While descriptive analysis was found to be the most commonly employed analytical technique, advanced techniques such as machine learning, artificial intelligence, and social network analysis are becoming more prominent. The studies addressed a wide range of elements associated with course evaluation, including course design, content quality, assignments, instructional strategies, workload, feedback mechanisms, and the integration of technology. These findings highlight the importance of adopting holistic approaches to capture the multifaceted nature of student experiences. This study also uncovers major limitations in the existing research, such as small sample sizes, potential biases due to the use of survey-based methods, and challenges in generalising findings across disciplines. These insights underscore the need for further research to enhance the methodologies used in course evaluations. This study contributes to advancing learning analytics practices and emphasises the importance of innovative approaches for evaluating and improving course effectiveness.

Design Networks: A Social Network Analysis of a Participatory Design Process

The paper aims to describe the collaborative relationships that are woven into a design process open to a plurality of actors from various disciplinary, cultural and organisational backgrounds who play various roles. To this end, a Social Network Analysis (SNA) has been conducted on a participatory design process in a Brazilian school community, intended to develop a hyperlocal social network for sharing knowledge among the community and the inhabitants of the region. The analysis of the design process explores the visualisation potential of sociograms. As a result, the paper elaborates on the concept of ‘design networks’, a form of organisation that allows many codesign processes and especially participatory ones. It also identifies three processes that are articulated in these networks, namely, the processes of ‘designing’, ‘infrastructuring’ and ‘seeding’.

The Role of Technical Car Features in Managing and Promoting New Peer-to-Peer Car-Sharing Systems: Insights from Potential Users and Strategic Implications for Service Providers

Peer-to-peer car-sharing systems are an evolving branch of urban mobility, aligning with global trends focused on sustainable development and reducing congestion in cities. A research gap has been identified concerning the specific vehicle attributes that would encourage the public to potentially use these services. Addressing this gap, and in the context of launching a new peer-to-peer car-sharing service in Katowice, Poland, this article investigates the technical features influencing the choice of vehicles in peer-to-peer car-sharing systems, particularly from the perspective of individuals who currently do not use such platforms. The study employs Social Network Analysis (SNA) to examine the interrelationships between vehicle attributes. The analysis reveals that key factors influencing users’ decisions include fuel/energy consumption, safety features, and technological advancement, with a particular emphasis on driver assistance systems, including autonomous driving capabilities. The network structure, characterized by a relatively low density (0.2536) and a short average path length (1.872), suggests that a few central vehicle features dominate user decisions, and improvements in these key areas can quickly propagate through the decision-making process, enhancing overall user satisfaction. To validate the findings, a Gradient Boosting Regression (GBR) analysis was conducted, confirming the significance of the key factors identified by the SNA, such as fuel efficiency, battery capacity, and safety systems, thus strengthening the reliability of the results. This study underscores the growing importance of sustainability and technological innovation in the automotive industry, particularly in the context of the sharing economy. It suggests that car-sharing platforms and vehicle manufacturers should prioritize these features to meet user expectations and preferences. These findings provide valuable insights for the strategic and operational management of peer-to-peer car-sharing services, emphasizing the importance of targeted vehicle selection and user-centered innovations to improve platform performance and scalability.

Blending positivity energy and fun: dominant discourse patterns of popular short videos in China’s mobile media communication

ABSTRACT The proliferation of mobile short video apps has transformed the dynamics of discourse circulation in contemporary society. However, limited research has been conducted on this phenomenon. This exploratory study employs topic modeling and social network analysis to examine discourse patterns within popular content (n = 10,000) on China’s leading mobile short video apps, Douyin and Kuaishou. The analysis reveals two primary discourse patterns: “positive energy” and “fun.” Remarkably, these popular short videos are predominantly produced by institutional-certified creators, including government departments and Chinese state-affiliated media, exerting a significant influence on mainstream discourse. These videos serve as essential instruments for the Chinese government to disseminate information and shape public opinion through mobile media platforms to a considerable degree. The tension between the “positive energy” and “fun” discourse patterns reflects the intricate nature of China’s short video mobile platforms under social media logic. This complexity is shaped by the entertainment-oriented nature of these platforms, government regulation, audience preferences, and the promotion of mainstream values within Chinese society.

Spatial Correlation Network and Influencing Factors of Carbon Emissions from Inter-provincial Tourism Transportation in China

Clarifying the spatial correlation network structure from tourism transportation carbon emissions and its influencing factors is crucial for China's tourism and transportation industry to coordinate the planning of carbon reduction governance and realize the sustainable development of the tourism transportation industry. Based on inter-provincial panel data from 2001 to 2021, China's carbon emissions from tourism transportation were measured, and the modified spatial gravity model was used to construct characteristics of provincial spatial networks and their influencing factors, which were analyzed using the social network analysis method and the QAP model. The study showed that ① China's total carbon emissions from tourism and transportation have been growing slowly year by year, showing a distribution pattern of "high in the southeast and low in the northwest," with obvious differences between the eastern and western regions. ② China's carbon emissions from tourism and transportation formed a multi-threaded and complex network of "dense in the east and sparse in the west." The "Matthew effect" in the spatial network was obvious, with eastern provinces such as Beijing, Shanghai, and Guangdong dominating the core and the northwestern and northeastern provinces such as Xinjiang, Qinghai, Heilongjiang, and Liaoning on the periphery. ③ China's carbon emissions from the tourism transportation block model had a clear division structure, and each block had a large number of correlations and received a spatial overflow of carbon emissions from other blocks. ④ Transportation energy intensity and transportation structure had a significant positive effect on the spatial correlation network, while spatial geographic distance, residents' consumption level, and tourism economic efficiency had a significant negative effect on the spatial network.

Social network analysis as a new tool to measure academic impact of physicians.

H-index is a widely used metric quantifying a researcher's productivity and impact based on an author's publications and citations. Though convenient to calculate, h-index fails to incorporate collaborations and interrelationships between physicians into its assessment of academic impact, leading to limited insight into grouped networks. We present social network analysis as a tool to measure relationships between physicians and quantify their academic impact. A bibliometric multicenter analysis was conducted on physician faculty from 129 US ACGME accredited otolaryngology programs who have publications with a physician co-author in the field. Using web searches, 2494 physician faculty were identified. Scopus IDs, h-indices, and publication data for these physicians were identified using multiple Elsevier APIs queried in December 2023. Publications with multiple otolaryngology physician co-authors were included. Network and sub network maps were generated using Gephi and analyzed with custom R scripts. Centrality measures (degree, PageRank, betweenness centralities) quantified collaboration propensity. Non-parametric correlation analysis between centrality measures and h-index was conducted. Sankey diagrams were plotted using ggplot2. A co-authorship network of 2259 physicians was constructed. Physicians were visualized as nodes with collaborations as links. Centrality measures correlated strongly with h-index (h-index vs. degree centrality: r 2 = 0.62, h-index vs. PageRank: r 2 = 0.55, h-index vs. betweenness centrality: r 2 = 0.55; p < .0001). Analysis revealed novel insights into physician network structure, identifying 14 communities primarily populated by single subspecialties with varied node density. Social network analysis showed moderate correlation between social connectedness measures and h-index, supporting its use in measuring academic impact. In otolaryngology, collaborative interactions within the academic community are strongly shaped by sub-specialty affiliation and academic institution.

Toward Personalized Digital Experiences to Promote Diabetes Self-Management: Mixed Methods Social Computing Approach.

Type 2 diabetes affects nearly 34.2 million adults and is the seventh leading cause of death in the United States. Digital health communities have emerged as avenues to provide social support to individuals engaging in diabetes self-management (DSM). The analysis of digital peer interactions and social connections can improve our understanding of the factors underlying behavior change, which can inform the development of personalized DSM interventions. Our objective is to apply our methodology using a mixed methods approach to (1) characterize the role of context-specific social influence patterns in DSM and (2) derive interventional targets that enhance individual engagement in DSM. Using the peer messages from the American Diabetes Association support community for DSM (n=~73,000 peer interactions from 2014 to 2021), (1) a labeled set of peer interactions was generated (n=1501 for the American Diabetes Association) through manual annotation, (2) deep learning models were used to scale the qualitative codes to the entire datasets, (3) the validated model was applied to perform a retrospective analysis, and (4) social network analysis techniques were used to portray large-scale patterns and relationships among the communication dimensions (content and context) embedded in peer interactions. The affiliation exposure model showed that exposure to community users through sharing interactive communication style speech acts had a positive association with the engagement of community users. Our results also suggest that pre-existing users with type 2 diabetes were more likely to stay engaged in the community when they expressed patient-reported outcomes and progress themes (communication content) using interactive communication style speech acts (communication context). It indicates the potential for targeted social network interventions in the form of structural changes based on the user's context and content exchanges with peers, which can exert social influence to modify user engagement behaviors. In this study, we characterize the role of social influence in DSM as observed in large-scale social media datasets. Implications for multicomponent digital interventions are discussed.