Social Network Analysis Studies Research Articles

Influence of Political Leaders in Five Countries on Climate Diplomacy via Twitter

Climate diplomacy is one of the hot topics in recent years that the world has paid extensive attention to. It goes beyond the traditional regional environmental issues; the worldwide impact of climate change makes international cooperation in this area more and more close. In order to explore the influence of political leaders on climate diplomacy and the reasons for their influence, this paper selects five countries with large differences in national conditions. And these countries will have a significant impact on international climate diplomacy in 2023. They are the United States, France, Brazil, Japan, and Senegal. This study quantitatively examines the data content analysis of climate-related tweets posted by political leaders from five countries on Twitter in 2023 and their social network influence through social network analysis and other quantitative studies to explore the influence of political leaders from five countries on international climate diplomacy. Finally, the conclusion is that the online power and influence of political leaders in each country do not exist independently but are closely linked to the real power of each country.

Bridging the Gap: Integrating Knowledge from the Study of Social Network Analysis and Infectious Disease Dynamics in Human and Nonhuman Primates

Primates live in complex social systems, and social contact and disease interact to shape the evolution of animal (including human) sociality. Researchers use social network analysis (SNA), a method of mapping and measuring contact patterns within a network of individuals, to understand the role that social interactions play in disease transmission. Here, we review lessons learned from SNA of humans and nonhuman primates (NHPs) and explore how they can inform health and wildlife conservation. Utilizing the breadth of knowledge in human systems and outlining how we can integrate that knowledge into our understanding of NHP sociality will add to our comprehension of disease transmission in NHP social networks and, in turn, will reveal more about human disease and well-being.

DCDIMB: Dynamic Community-based Diversified Influence Maximization using Bridge Nodes

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static social network. But in real life, social networks are dynamic in nature. This work addresses the diversification of activated nodes in the dynamic social network. This work proposes an objective function that maximizes the number of communities by utilizing bridge nodes. We also propose a diffusion model that considers the role of inactive nodes in influencing a node. We prove the submodularity, and monotonicity of the objective function under the proposed diffusion model. This work analyzes the impact of different ratios of bridge nodes in the seed set on real-world and synthetic datasets. Further, we prove the NP-Hardness of the objective function under the proposed diffusion model. The experiments are conducted on various real-world and synthetic datasets with known and unknown community information. The proposed work experimentally shows that the objective function gives the maximum number of communities considering bridge nodes compared to the benchmark algorithms.

Faculty members as academic knowledge brokers in Iran's health sector: a social network analysis study

BackgroundInteraction between researchers and policymakers is an essential factor to facilitate the evidence-informed policymaking. One of the effective ways to establish this relationship and promote evidence-informed policymaking is to employ people or organizations that can play the role of knowledge brokers. This study aims to analyze the communication network and interactions between researchers and policymakers in Iran's health sector and identify key people serving as academic knowledge brokers.MethodsThis study was a survey research. Using a census approach, we administered a sociometric survey to faculty members in the health field in top ten Iranian medical universities to construct academic-policymaker network using social network analysis method. Network maps were generated using UCINET and NetDraw software. We used Indegree Centrality, Outdegree Centrality, and Betweenness Centrality indicators to determine knowledge brokers in the network.ResultsThe drawn network had a total of 188 nodes consisting of 94 university faculty members and 94 policymakers at three national, provincial, and university levels. The network comprised a total of 177 links, with 125 connecting to policymakers and 52 to peers. Of 56 faculty members, we identified four knowledge brokers. Six policymakers were identified as key policymakers in the network, too.ConclusionsIt seems that the flow of knowledge produced by research in the health field in Iran is not accomplished well from the producers of research evidence to the users of knowledge. Therefore, it seems necessary to consider incentive and support mechanisms to strengthen the interaction between researchers and policymakers in Iran's health sector.

Peer technical support in preservice teacher education: A mixed methods social network analysis and phenomenological study to understand relative expertise

Peer technical support in preservice teacher education: A mixed methods social network analysis and phenomenological study to understand relative expertise

Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and to assess how well AI-based machine learning and deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study to obtain understanding of the current research trends, citation patterns, ownership and partnership arrangements, publishing patterns, and other parameters related to early identification of apple illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted to any solitary arena of research, despite the fact that there have been many studies on the identification of apple diseases. By employing a scientometric technique and 109 publications from the Scopus database published between 2011 and 2022, this study attempted to assess the condition of the research area and combine knowledge frameworks. To find important journals, authors, nations, articles, and topics, the study used the automated processes of VOSviewer and Biblioshiny software. Patterns and trends were discovered using citation counts, social network analysis, and citation and co-citation studies.

The influence of peer's social networks on adolescent's cannabis use: a systematic review of longitudinal studies.

A systematic review was performed to summarize the key findings of the peer influence on cannabis use through Social Network Analysis (SNA) studies and identify limitations and gaps with the purpose of informing future research and practice. Longitudinal studies were included since they provide robust information about social relationships change over time. Adolescents' cannabis use is a global problem, which has awakened an interest in its determinants such as social influences. Research has shown the importance of these influences on cannabis uptake and use. SNA is an useful relational approach to examine socialization mechanisms related to the onset of cannabis use in adolescents. A search was conducted in PyscINFO, PubMed, Scopus and Web of Science for longitudinal articles published until February 2023, to examine cannabis use and peer's social networks. We focus on peers' influence of peers on cannabis use. Additionally, information about effect of cannabis use for peer selection was collected. The results of the included studies (n = 8) showed that friends' cannabis use was most often/strongly associated with cannabis use. There was also an increase of cannabis use when the adolescent did not feel close to the school's peers, had a higher proportion of friendships relative to the total number of ties in the neighborhood, had a central position, did not belong to any group but had ties to members of two or more groups, had cannabis user friends (especially in early ages), and lived in a neighborhood where cannabis was used. Cannabis use is mainly related to friends' use. Yet, future studies are warranted to control for relevant selection effects to further knowledge on network effects on cannabis use, improving the design, and improving the modeling of the network. This systematic review may inform about the critical aspects of preventing cannabis use among adolescents, taking into consideration their complex social environment.

Who chooses whom for professional interaction? A sociometric inquiry into teacher leadership

ABSTRACTGiven the dyadic and multi-layered nature of professional relations, informal teacher learning mostly goes through complex, non-linear, and context-dependent professional interactions. Advice-seeking relationships (ASRs) are the most common form of informal interactions between teachers. However, due mostly to analytical constraints, little is known about the factors influencing ASRs in school settings. The paper aims to provide an empirical base for understanding the informal leadership exercised through between-teacher ASRs around technological and pedagogical expertise. To that end, inferential social network analysis (SNA) techniques are employed. Results showed teacher perception of positive school culture promotes pedagogical ASRs. Administrative leadership is significant in one-way advice flows from administrators to subordinates. Interpersonal similarities by gender and teaching experience are influential in peer selection for ASRs. Hybrid-expertise teachers, being equally headed for technological and pedagogical advice, occupy more central position in advice-networks. Regardless of school culture, technology-savvy teachers are more preferred for ASRs. The more teachers provide colleagues with technological advice, the more they assume a leading position for pedagogical advice, but not vice versa. Overall, the present SNA study sheds light on relational complexities of teacher leadership for professional learning that otherwise might not be salient when investigated using linear models on individual metrics.

Discovering Key Actors and Opinion Leaders on Twitter’s Start-Up and Entrepreneurship Topics Trending: A Social Network Analysis Approach

The topic of start-ups and entrepreneurship has been widely discussed on social media to disseminate information while serving as a digital marketing platform. Unfortunately, researchers and practitioners interested in analyzing social networks to gain business advantage through digital platforms still need to be expanded. This paper analyzes networks on social media to find important actors and opinion leaders on trending start-up and entrepreneurship topics. Data analytics is performed using one of the most extensive social media, Twitter. Several visualizations of the social network of these actors are then displayed using specific network visualization applications. The results are discussed by analyzing the main attributes in the network, namely degree, centrality, modularity, and word cloud, which can lead us to discover important attributes in the network: main actors, opinion leaders, groupings of actors according to topics discussed, and specific terms that are trending topics. This paper contributes to digital marketing practitioners and social network analysis studies in related fields with a concise and duplicable methodology. Further research is recommended to work more extensively with more representative data from multiple networks to analyze these topics in more depth.

On Manipulating Weight Predictions in Signed Weighted Networks

Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less understood in this respect. In this paper, we focus on the problem of evading FGA---an edge weight prediction method for signed weighted networks by Kumar et al. 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of FGA and its computational properties in terms of manipulability. Our positive finding is that, unlike many other tools, this measure is not only difficult to manipulate optimally, but also it can be difficult to manipulate in practice.

Informal Caregiving Networks of Older Adults With Dementia Superimposed on Multimorbidity: A Social Network Analysis Study.

Caregiving is a social process and commonly involves more than a single caregiver, especially for older adults with multimorbidity, including dementia. This study was to characterize informal caregiving networks of older adults with dementia superimposed on multimorbidity (e.g., end-stage kidney disease) and to examine the relationships of network properties to outcomes of caregivers and older adults. An egocentric social network survey was conducted. Up to 3 family caregivers of older adults on dialysis who had moderate-to-severe irreversible cognitive impairment with or without a documented diagnosis of dementia were recruited from 11 dialysis centers in 2 states. Caregivers completed a social network survey about individuals providing caregiving to the older adult and measures of caregiving burden and rewards, depression, and financial hardship. Older adults' emergency department visits and hospital admissions during the past 12 months were abstracted from the medical records. A total of 76 caregiver informants of 46 older adults (78% Black) participated in the study. Of the 46 older adults, 65% had a multimember network (median size of 4). As the network density (the proportion of ties between members among all possible ties) increased, primary caregivers' financial hardship decreased whereas nonprimary caregivers' financial hardship increased. Further, for every 1-unit increase in mean degree (the average number of connections among members), there was a nearly fourfold increase in the odds of no hospital admission during the prior year for the older adult. The network dynamics of informal caregiving networks may have an impact on the well-being of caregivers and older adults with dementia, but confirmatory longitudinal studies are needed.

Conselhos de Administração Interligados: Um Tutorial de Análise de Redes Sociais

ABSTRACT Objective: the literature on board interlocks has increased in recent years, focusing on understanding board composition and its relationships with other companies’ boards. Such studies usually require multiple procedures of data extraction, handling, and analysis to create and analyze social networks. However, these procedures are not standardized, and there is a lack of methodological instructions available to make this process easier for researchers. This tutorial intends to describe the logical steps taken to collect data, treat them, and map and measure the network properties to provide researchers with the sources to replicate it in their own research. We contribute to the literature in the management field by proposing an empirical methodological approach to conduct board interlocks’ research. Proposal: our tutorial describes and provides examples of data collection, directors’ data treatment, and the use of these data to map and measure network structural properties using an open-source tool - R statistical software. Conclusions: our main contribution is a tutorial detailing the steps required to map and analyze board interlocks, making this process easier, standardized, and more accessible for all researchers who wish to develop social network analysis studies.

Introduction to the special issue: Social network analysis and its application to schools.

Social network analysis (SNA) consists of a set of theories and methods that considers the direct and relationships in a clearly defined context. Although SNA research has witnessed rapid growth in the social sciences, school psychology has not kept pace. The lack of SNA studies in school psychology journals is interesting given that many topics of interest in the field both influence-and are influenced by-the social context. This special issue showcases how an array of SNA theories and methods can be used to determine the antecedents of relationships and the effects of these relationships on important outcomes. Each article was selected to highlight how SNA can shed unique insights on how social interactions can influence school aggression, friendships, language skills development, same- and cross-ethnic relationships and school belonging, and others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Fabric of Resistance: Textile Workshops and the Rise of Rebellious Landscapes in Colonial Peru

This monograph uncovers the fascinating story of the textile workshop (obraje) in Pomacocha, located in the Ayacucho region, Peru. After its foundation in 1689, the workshop developed into a thriving hub of textile production fueling the vibrant cross-Andean cloth trade. More than a hundred working families lived in Pomacocha, subjugated to the colonial regimes of debt slavery, forced labor, bondage, and prison labor. The Fabric of Resistance is a compelling, rich, and provocative study, based on a wide range of evidence and a dazzling mix of methods—ranging from archaeological analysis, political geography, social network analysis, archival research, and demographic studies to spatial analysis. The volume situates textile production at the heart of processes of identity formation in the colonial Andes. The obraje, Hu shows, was the arena where identities, communities, and social cohesion took on shape, and where colonial rule, as well as opposition, got negotiated. The volume knots together the history of labor, resistance, race, and colonialism, narrated through the lens of the changing social conditions of textile production.

Assessing negotiation skill and its development in an online collaborative simulation game: A social network analysis study

AbstractSimulation games are widely used to teach negotiation skills in political science education. However, existing studies focus on the impact of simulation games on students' knowledge gains and affective changes and largely ignore skill assessment and development in the gaming environment. This study aimed to understand the process of student groups practicing negotiation skills in a collaborative simulation game through social network analysis (SNA). We proposed a conceptual framework to assess negotiation skills by identifying different negotiation skillsets based on participatory roles in collaborative learning, investigated the skill development through the change of the skillsets over time, and examined the relationship between the negotiation skillsets and achievement. The results showed that the majority of student groups practiced more complex negotiation skillsets towards the end of the game, and the complexity of skillsets was positively related to the negotiation outcomes in the simulation game. The study demonstrated the possibilities of using SNA as an analytical tool to measure negotiation skills and explore dynamic skill development in a collaborative simulation game. It has also shown the potential of integrating SNA in a collaborative gaming environment for automated analysis of a large volume of data concerning interactions.Practitioner notesWhat is already known about this topic Simulation games are widely used to teach negotiation skills in political science education to improve the connection between theoretical knowledge and skill practice. Existing studies focus on the impact of simulation games on learners' knowledge gains and affective changes but ignore the skill assessment and development in the gaming environment, especially in the collaborative simulated gaming environment. What this paper adds The study investigated the process of student groups practicing negotiation skills in a collaborative simulation game through Social Network Analysis (SNA). We proposed a novel conceptual framework to measure negotiation skills by identifying different negotiation skillsets through connecting them to learners' participatory roles that emerged during the collaborative gameplay. The study demonstrated learners' dynamic and evolutionary process of practicing negotiation skills in the game. The results showed that there was a positive relationship between the complexity of negotiation skillsets and negotiation outcomes in the game. Implications for practice and/or policy The study demonstrates the possibilities of using SNA as an analytical tool to measure negotiation skills and explore dynamic skill development in a collaborative gaming environment. The results can guide teachers to identify risky game moves and students in need and provide personalized feedback to help improve students' negotiation skills. The findings can help teachers optimize the game design to ensure students' equal and active participation in the game. The study highlights the potential of integrating SNA in collaborative gaming environments for automated analysis of a large volume of data concerning interactions.

Reprint of: Network Canvas: Key decisions in the design of an interviewer-assisted network data collection software suite

Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software – a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications: Architect: an application for researchers to design and export interview protocols; Interviewer: a touch-optimized application for loading and administering interview protocols to study participants; and Server: an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations.

Social Network Analysis and Literary Studies

사회 연결망 분석(social network analysis)은 20세기 내내 사회과학 분야에서 지속적으로 발전해온 사회구조 분석 이론이자 방법으로서 개체, 조직, 인간, 사물 사이의 복잡다기한 연결을 거시적, 미시적으로 분석하기 위해 관계형 데이터를 구축하고 이를 연결망으로 시각화하여 유동적이면서도 구조화된 전체 장을 파악하고자 하는 분야이다. 이러한 사회 연결망 분석은 최근 인문학 연구 분야에서, 특히 2000년대 이후 디지털인문학이 각광을 받기 시작하면서 그 방법 중 하나로 주목받고 있다. 영미문학 연구 분야에서도 사회 연결망 분석의 이론과 기법을 영문학 자료에 적용하여 그 동안 전통적인 연구 방법으로 파악하기 어려웠던 특정 국면의 문학 관계망을 복원한 성과들이 나타나고 있다. 한국의 국문학 연구 분야 역시 사회 연결망 분석 기법을 적용한 사례가 드물지만 꾸준히 등장하고 있다. 본 논문은 사회 연결망 분석 이론 및 방법이 문학 연구에 지닌 함의와 가능성에 주목하고 이를 체계적으로 소개하여 한국 학계의 디지털인문학적 문학 연구를 활성화하는 데 기여하고자 한다. 먼저 사회 연결망 이론을 소개하며 그 의미와 역사를 되짚어 본다. 그리고 이것이 인문학, 그 중에서도 문학 연구에 어떻게 활용될 수 있는지, 그 가능성은 무엇인지 등을 점검하기 위해 영미문학 분야 연구 성과들을 비판적으로 점검한다. 나아가서 사회 연결망 분석이 국문학계에서는 어떤 성과들을 축적하고 있는지 검토하여 한국에서의 사회 연결망 분석 기법을 활용한 문학 연구의 가능성을 모색한다.Social network analysis has been evolving as a social structure analysis theory and methodology in the social science discipline throughout the 20th century. It strives to understand the complex networked relationships among entities such as objects, individuals, social groups, organizations, etc. by preparing and visualizing their relational data so as to grasp the fluid dynamics of any given field. Many humanities scholars have also paid close attention to social network analysis as a new research method as digital humanities emerges as a formidable discourse in the new millennium. There are quite a few remarkable cases that apply social network analysis concepts and techniques to English literary data and thus manage to delineate literary networks at the particular junctures in literary history, which might have remained opaque otherwise. A similar trend can be observed in Korean literary studies though only a small number of researchers are interested in the method. This paper introduces social network analysis, focusing on its possibilities as a new literary research method and hence facilitating digital humanities approaches to literature in Korean academia. First, it briefly presents social network analysis theory, its history and concepts, and its methodological significance. And then, it goes on to critically review the actual research cases in the fields of English and Korean literature to examine how social network analysis can be employed in literary scholarship and what potentials it contains as a research method. Finally, this paper suggests that social network analysis helps deepen our knowledge and appreciation of literature by engaging literary history with a fresh look.

Gravity and depth of social media networks

Abstract Structures of social media networks provide a composite view of dyadic connectivity across social actors, which reveals the spread of local and global influences of those actors in the network. Although social media network is a construct inferred from online activities, an underlying feature is that the actors also possess physical locational characteristics. Using a unique dataset from Facebook that provides a snapshot of the complete enumeration of county-to-county connectivity in the USA (in April 2016), we exploit these two dimensions viz. online connectivity and geographic distance between the counties, to establish a mapping between the two. We document two major results. First, social connectivity wanes as physical distance increases between county-pairs, signifying gravity-like behaviour found in economic activities like trade and migration. Two, a geometric projection of the network on a lower-dimensional space allows us to quantify depth of the nodes in the network with a well-defined metric. Clustering of this projected network reveals that the counties belonging to the same cluster tend to exhibit geographic proximity, a finding we quantify with regression-based analysis as well. Thus, our analysis of the social media networks demonstrates a unique relationship between physical spatial clustering and node connectivity-based clustering. Our work provides a novel characterization of geometric distance in the study of social network analysis, linking abstract network topology with its statistical properties.

Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study.

BackgroundThe dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users’ cognition as well as decision-making and interaction behaviors.ObjectiveThis study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users’ responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber’s impact on the information characteristics of user interaction content.MethodsWe used Sina Weibo’s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals’ attitudes when retweeting or commenting on others’ tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users’ interaction content from three dimensions—sentiment expression, information seeking and sharing, and civility—to test the impact of the echo chamber effect.ResultsOur results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users’ information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility.ConclusionsOur findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.

Network Canvas: Key decisions in the design of an interviewer-assisted network data collection software suite

Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software – a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications: Architect: an application for researchers to design and export interview protocols; Interviewer: a touch-optimized application for loading and administering interview protocols to study participants; and Server: an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations.