Social Network Visualization Research Articles

The tortoise in the urban development network: How social relations affect decision-making speed in a road expansion project in Kristiansand, Norway

ABSTRACT Why do urban development projects take a long time to complete? This question is of paramount importance, both practically and academically. Practically, understanding the reasons for delays in these projects can help improve project efficiency, reduce costs, and enhance stakeholder satisfaction. Academically, the study contributes to the field of urbanization by applying social network analysis (SNA) to explore the interactions and relationships among decision-makers in urban development projects. This study draws upon original data from a road extension project in Kristiansand to illustrate and explain why the implementation of large development projects suffers from convoluted and slow decision-making processes after an overall agreement on project aims and goals has been established. This study draws on a sample of 33 actors who participated in the decisions concerning the project. I collected the data through a combination of interviews and document reviews and subsequently analyzed them using social network structural metrics and visualization techniques. The results show that the actors are connected in less dense, more centralized, and clustered networks with subgroups and low reciprocity which have hampered efficient communication, resulting in delays. The findings imply that the pattern of relationships among decision-making actors can affect the amount of time taken to reach conclusions in urban development initiatives.

Issues Related to the Use of Visual Social Networks and Perceived Usefulness of Social Media Literacy During the Recovery Phase: Qualitative Research Among Girls With Eating Disorders.

The patient-centered approach is essential for quality health care and patient safety. Understanding the service user's perspective on the factors maintaining the health problem is crucial for successful treatment, especially for patients who do not recognize their condition as clinically relevant or concerning. Despite the association between intensive use of visual social media and body dissatisfaction and eating disorders, little is known about the meanings users assign to posting or searching for edited photos and the strategies they use to protect themselves from digital risks. This study aims to examine how young women recovering from eating disorders in Northern Italy perceive the health risks and potential benefits associated with visual social networks (ie, Instagram and Snapchat). The literature has found these platforms to be detrimental to online body comparisons. It also explores the perceived usefulness, willingness, and personal interest in coconstructing social media literacy programs with girls recovering from eating disorders. A total of 30 semistructured interviews were conducted with adolescent girls aged 14-17 years at the end of their treatment for eating disorders. The following areas of research were addressed: (1) the meanings associated with the use of Instagram and Snapchat; (2) the investment in the photographic dimension and feedback; (3) the impact of visual social networks on body experiences; (4) the potential and risks perceived in their use; (5) the importance of supporting girls undergoing treatment for eating disorders in using social networks; and (6) the usefulness and willingness to co-design social network literacy programs. Content analysis was applied. A total of 7 main contents emerged: active or passive role in using social networks, the impact of online interactions on body image, investment in the photographic dimension, effects on self-representation, perceived risks, self-protective strategies, and potential benefits. The findings highlight a strong awareness of the processes that trigger body comparisons in the virtual context, creating insecurity and worsening the relationship with oneself. The self-protective behaviors identified are the development of critical thinking, the avoidance of sensitive content, increased control over social networking site use, and a certain skepticism toward developing antagonistic ideologies. All these topics were considered fundamental. The findings provide important insights for health professionals working with youth in preparing media literacy programs. These programs aim to reduce potential risks and amplify the positive effects of online resources. They underscore the importance of addressing this issue during hospitalization to develop skills and critical thinking aimed at changing small habits that perpetuate the problem in everyday life. The inherent limitations in current service practices, which may not adequately address individual needs or impact posttreatment life, must also be considered.

Enhancing team dynamics through digital coaching: the role of managerial and peer support

PurposeAlthough the literature on the effectiveness of team interventions is constantly expanding, there has been a strong focus on the process mechanisms that could explain their success, often overlooking the contextual aspects in which these interventions are carried out. Based on the Context-Mechanism-Outcome framework, this study aims to investigate the influence of contextual factors on the effectiveness of digital team coaching interventions that use social network visualisation to enhance team coordination and reduce interpersonal conflicts.Design/methodology/approachUsing a multi-wave, longitudinal design, this research analysed 38 work teams from three organisations over three-time points. Data collection focused on manager and peer support, the mechanisms of training transfer and action plan implementation and the outcomes of these interventions. Surveys were administered in three organisations, involving 317 respondents across different phases. The intervention spanned six to eight months, incorporating three to four structured online group sessions. Each session involved a multi-stage process, concluding with a result-oriented action plan about work-related goals. The intervention included social network visualisation, discussions, coaching and continuous refinement of action plans.FindingsThe analysis highlights how manager support significantly correlates with team coordination and performance, mainly when teams are less engaged in implementing action plans. Peer support did not show a mediating effect on training transfer or outcomes but had direct positive impacts on team coordination and performance.Practical implicationsTeams actively implementing action plans may require less immediate managerial support for effective coordination and high performance. The manager’s role becomes crucial, particularly in the early stages of the intervention or in those teams where online coordination alone may not be adequate for action plan implementation. Peer support for training transfer could enhance the effectiveness of the intervention in achieving desired team outcomes; therefore, cultivating a supportive peer environment is crucial for the success of such interventions. Monitoring and assessing team dynamics are vital to maximise the benefits of digital team coaching interventions.Originality/valueThis study stands out for its innovative exploration of the interplay between managerial and peer support in the context of digital team coaching, using social network visualisation as a novel approach to enhancing team dynamics.

A Snapshot of Academic Job Placements in Linguistics in the US and Canada

Abstract Most people working in the field of linguistics in the US and Canada have an intuitive sense of who the “major players” are among PhD-granting linguistics departments. Our analysis demonstrates that the frequently-perceived hierarchy of linguistics programs is indeed correct. Drawing on publicly available information from Winter/Spring, 2019 on faculty at all PhD-granting linguistics programs across the US and Canada, we use social network and heat map visualizations to demonstrate the existence of an extraordinarily strong and relatively stable hierarchy of programs whose graduates dominate the linguistics academic job market. A secondary finding is that many of the top programs are characterized by gender imbalances. We argue that the top programs’ tremendous influence on the job market as a whole affords these programs the ability – indeed, the responsibility – to take the lead in effecting positive change in the field's hiring patterns more broadly.

Deep Attentional Implanted Graph Clustering Algorithm for the Visualization and Analysis of Social Networks

As the user base expands, social network data becomes more intricate, making analyzing the interconnections between various entities challenging. Various graph visualization technologies are employed to analyze extensive and intricate network data. Network graphs inherently possess intricacy and may have overlapping elements. Graph clustering is a basic endeavor that aims to identify communities or groupings inside networks. Recent research has mostly concentrated on developing deep learning techniques to acquire a concise representation of graphs, which is then utilized with traditional clustering methods such as k-means or spectral clustering techniques. Multiplying these two-step architectures is challenging and sometimes results in unsatisfactory performance. This is mostly due to the lack of a goal-oriented graph encoding developed explicitly for the clustering job. This work introduces a novel Deep Learning (DL) method called Deep Attentional Implanted Graph Clustering (DAIGC), designed to achieve goal-oriented clustering. Our approach centers on associated graphs to thoroughly investigate both aspects of data in graphs. The proposed DAIGC technique utilizes a Graph Attention Autoencoder (GAA) to determine the significance of nearby nodes about a target node. This allows encoding a graph's topographical structure and node value into a concise representation. Based on this representation, an interior product decoder has been trained to rebuild the graph structure. The performance of the proposed approach has been evaluated on four distinct types and sizes of real-world intricate networks, varying in vertex count from 𝑁=102 𝑡𝑜 𝑁=107. The performance of the suggested methods is evaluated by comparing them with two established and commonly used graph clustering techniques. The testing findings demonstrate the effectiveness of the proposed method in terms of processing speed and visualization compared to the state-of-the-art algorithms.

"Trust people you've never worked with" - A social network visualization of teamwork, cohesion, social support, and mental health in NHS Covid personnel.

The unprecedented rapid re-deployment of healthcare workers from different care pathways into newly created and fluid COVID-19 teams provides a unique opportunity to examine the interaction of many of the established non-technical factors for successful delivery of clinical care and teamwork in healthcare settings. This research paper therefore aims to address these gaps by qualitatively exploring the impact of COVID work throughout the pandemic on permanent and deployed personnel's experiences, their ability to effectively work together, and the effect of social dynamics (e.g., cohesion, social support) on teamwork and mental health. Seventy-five interviews were conducted across the UK between March and December 2021 during wave 2 and 3 of COVID-19 with 75 healthcare workers who were either permanent staff on Intensive Care/High Dependency Units used as COVID wards, had been rapidly deployed to such a ward, or had managed such wards. Work Life Balance was measured using the WLB Scale. Interview transcripts were qualitatively coded and thematic codes were compared using network graph modeling. Using thematic network analysis, four overarching thematic clusters were found, (1) teamwork, (2) organizational support and management, (3) cohesion and social support, and (4) psychological strain. The study has three main findings. First, the importance of social factors for teamwork and mental health, whereby team identity may influence perceptions of preparedness, collaboration and communication, and impact on the collective appraisal of stressful events and work stressors. Secondly, it demonstrates the positive and negative impact of professional roles and skills on the development of teamwork and team identity. Lastly the study identifies the more pronounced negative impact of COVID work on deployed personnel's workload, mental health, and career intentions, exacerbated by reduced levels of social support during, and after, their deployment. The thematic network analysis was able to highlight that many of the traditional factors associated with the successful delivery of patient care were impeded by pandemic constraints, markedly influencing personnel's ability to work together and cope with pandemic work stressors. In this environment teamwork, delivery of care and staff well-being appear to depend on relational and organizational context, social group membership, and psycho-social skills related to managing team identity. While results hold lessons for personnel selection, training, co-location, and organizational support during and after a pandemic, further research is needed into the differential impact of pandemic deployment on HCWs mental health and teamwork.

Advancements in Social Network Analysis and Visualization: A Comprehensive Survey

Advancements in Social Network Analysis and Visualization: A Comprehensive Survey

Interventions to reduce the negative impact of online highly visual social networking site use on mental health outcomes: A scoping review.

Interventions to reduce the negative impact of online highly visual social networking site use on mental health outcomes: A scoping review.

Visual Representations of Sexual Minorities in Chinese Newspapers

Same-sex sexuality remains a prevalent taboo in many cultures and societies globally. In the past decade, scholars from various fields have increasingly examined how sexual minorities (SMs) are represented. The present paper contributes to this growing research by addressing two notable gaps. First, since much of the existing research centers on Western contexts, there is limited study on the portrayal of SMs within the Chinese cultural framework. Second, past studies emphasize verbal portrayals, often sidelining the importance of visual representations. This article will report and analyze the photos gathered from two prominent Chinese newspapers, China Daily and Global Times, pertaining to SMs in mainland China. By drawing on Van Leeuwen’s visual social actor network, the article reveals that Chinese SMs most often are visually represented as victims, disguisers, warriors, and elitists. Finally, we explore the socio-cultural influences behind these representation patterns.

Digital team coaching for workplace communication: longitudinal evaluation of recipients’ perceptions

PurposeThe purpose of this study is to describe the implementation of a digital-based team coaching intervention aimed at improving team communication in the workplace through social network visualization. The study examined recipients’ perceptions of the intervention at two time points and assessed the temporal stability of various factors, including the intervention’s integrity, design, transferability, acceptance and the usability of the adopted visualization tool. The moderating role of digital usability was also evaluated.Design/methodology/approachFour team coaching sessions were delivered to 62 participants from seven teams across three departments within a large public health-care organization in Northern Italy. Perceptions of the intervention dimensions were collected after the second and fourth sessions.FindingsResults indicated that, at both time points, recipients appreciated the intervention’s integrity and usability more than its design, transferability and acceptance. Furthermore, no significant changes in recipients’ perceptions were observed over time. The transferability of the intervention was significantly associated with its acceptance, but only when the usability of the digital tool was high.Research limitations/implicationsThe study enriches existing literature on digital interventions in group communication by focusing on process dimensions like recipients’ perceptions of various aspects and the implementation process. Furthermore, the study underscores the potential of integrating specific techniques such as sociomapping and coaching within health-care organizations, encouraging more research and development in these areas.Practical implicationsThe study emphasizes the critical role of usability and integrity in digital-based team coaching interventions, suggesting that high-quality, user-friendly tools not only lead to initial effectiveness but also sustain positive impacts over time, while also increasing transferability and acceptance.Originality/valueThe present study uniquely deploys a longitudinal approach to examine recipients’ perceptions of a digital-based intervention that combines social network visualization and team coaching to enhance team communication.

Joint Latent Space Model for Social Networks with Multivariate Attributes.

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation-maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package "jlsm" is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf .

How did sustainable Spanish and Italian brands communicate COVID-19 on social media?

Purpose This study aims to determine how the most sustainable brands in Italy and Spain developed communication and awareness-raising actions during the COVID-19 pandemic and whether these companies were truly involved in raising public awareness of the pandemic. The authors specifically focus on sustainable companies, as health communication is considered to be an important corporate social responsibility task. Design/methodology/approach To answer these questions, the authors have used data on the social media activity (Twitter and Instagram) of these brands in Spain and Italy, extracting the posts and associated hashtags that each of them has published throughout the pandemic to be processed using social network analysis and visualization techniques. Findings The detailed analysis of both the levels of activity and the content of the messages provides interesting insights into the communication models of the companies and the influence of factors such as time, country and the specific social media platform used. Originality/value The authors analyze the communication of the most sustainable businesses on social media during the pandemic, adopting a highly innovative approach. The particular originality of this study lies in the parallel analysis of two different countries that were simultaneously shaken by the pandemic in very similar circumstances. This study also presents a novel use of graphical representation tools in terms of companies’ behavior for health communication on social media.

Analysis of Construction Networks and Structural Characteristics of Pearl River Delta and Surrounding Cities Based on Multiple Connections

The Pearl River Delta (PRD) is one of three world-class city clusters in China, which is important for the strategical deployment of the national “Belt and Road”. Based on nighttime lighting data, Baidu index, and train stopping times, this study analyzed the network of spatial patterns and structural evolution of the PRD and surrounding cities via social network analysis and dynamic network visualization, providing new perspectives and ideas for the study of intercity linkages and urban networks. The results provide decision support to the government for urban cluster planning. From 2014 to 2020, the economic network evolved from a uniaxial structure to an “inverted V” structure. The transportation network evolved from a uniaxial structure to a “△” structure. The information network did not show any obvious structural changes during its development, except for a star-shaped radial structure. The PRD city cluster and its surrounding cities exhibited a spatially non-uniform distribution in terms of spatial connections. The total connections between Guangzhou and Foshan and the surrounding cities in terms of economic, transportation, and information functions account for 30%, 28%, and 10% of the total urban connections, respectively. The graph entropy growth rates of the PRD city cluster and surrounding cities in economic, transportation, and information networks from 2014 to 2020 were 39.9%, 115.4%, and 5.1%, respectively. The network structures of economic and transportation networks stabilized eventually. The information network structures are stable.

Unreal memories

Images of architecture spread quickly on visual social networks that gain interest to disseminate and communicate architecture. For this reason, it is a must to diagnose the social construction of the image of architecture, to explore the restrictions and publication formats of the platform, to identify the reaction and dissemination habits in relation to the visual features of photography and to delve into the possibilities and the risks introduced by artificial intelligence systems. The research method follows a cognitive association approach through various graphic case studies on Instagram. The exploratory design to approach the role of the image to communicate architecture in visual social media arises from the concept of the fragment and the biased vision, a division required due to the nature of related image suggestions as a browsing method on Instagram. The findings identify opportunities and action strategies to complement the contents that build the collective image of architecture adapted to the logic of the new scenario.

Spam community detection & influence minimization using NRIM algorithm

Big Data is a research area where many different disciplines work together. Social media has grown in popularity as a tool for disseminating and gathering information. However, the success of social media like Twitter, Facebook, etc., has not only attracted genuine users but also spammers who utilize social graphs, famous phrases, and hashtags to spread malware. This study uses several social network analysis and visualization methods based on bibliometric data from the Web of Science to look at the structure and patterns of interdisciplinary collaborations and the latest emerging overall practice. For a better understanding of spamming behaviors on Twitter, the Twitter data set is thoroughly analyzed, and categorized into Spam and Non-Spam classifications. Earlier studies confined their scope to investigating the most negatively influential spammers by blocking the most influential spammers. However, the cumulative impact of other spammers having low individual negative influence values but higher impact values was neglected. In this article, we develop an algorithm for detecting social spam using Node Rank-based Influence Minimization (NRIM), which integrates Node Rank with the impact value of spam. The proposed spam influence minimization model also identifies spam-influential users and aids in limiting the flow of spam tweets within the Twitter network. Additionally, a detection algorithm for influential communities has been proposed to limit the spread of spam content through influential communities on the Twitter network. The primary focus of this paper is to reduce the spam impact on Twitter data by identifying influential spammers using the Node_Rank-based Influence Minimization (NRIM) algorithm. To begin, the tweets are classified into spam and non-spam using a machine learning algorithm. Furthermore, the spam observed in the Graph is analyzed, and the Spammer is passed through the NRIM algorithm to find the influential Spammers. In addition to this, the negative impact of the Spammer is reduced on the Twitter graph, and its impact is analyzed on query processing executed on Graph. The technique used for the minimization of the Spammer’s negative effect on the graph reduces the query execution time by 12%.

Role of visual social networking, hypersensitive narcissism, and anxiety sensitivity in online compulsive buying

The impact of hypersensitive narcissism and anxiety sensitivity on online compulsive buying were examined. These transdiagnostic personality factors have not previously received attention in virtual, as opposed to real-world, contexts, and the study explored their interaction with the use of visual and textual social media platforms for online buying. The present study sampled 440 participants (264 female; 174 male), aged 18 – 37 years. Participants completed an online survey comprising: Online Edwards Compulsive Buying Scale Revised, Hypersensitive Narcissism Scale, and the Anxiety Sensitivity Index-3. Visual social networking usage was a risk factor for online compulsive buying, and there were significant positive relationships between both hypersensitive narcissism, and anxiety sensitivity, with online compulsive buying. Anxiety sensitivity mediated the relationship between hypersensitive narcissism and compulsive online buying when the dominate form of social media use was textual, but not when it was visual.

How brain networks tic: Predicting tic severity through rs-fMRI dynamics in Tourette syndrome.

Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. The relevant predictors pointed to dysfunction of the primary motor cortex, the prefrontal-basal ganglia loop and amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.

Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

Understanding the Diffusion of Theoretical Knowledge in Nursing: A Citation Analysis of Meleis's Transition Theory.

The aim of this study is to examine the structure of the knowledge creation process in nursing science and to investigate the dissemination of theoretical knowledge in other disciplines by analyzing citations and social network data. This exploratory study evaluated Metadata to find academic publications. Meleis's Transition Theory was selected as a case study. A majority of the publications that represented the transition theory were assigned as the core of the theory. Forward and backward citations were used as agents of knowledge linkage to determine the dissemination of the theory in the field of science. Social network analysis and visualization were used to depict graphical and structural relations of the research front and the knowledge base. The knowledge base of the Transition Theory was built on 7 different information networks. The most effective and vast information network consisted of Meleis, AI as a researcher and the central information network, which is the journal of Nursing Research. Oncology, geriatrics, public health, and psychology in the areas of information propagation within the field of nursing were considered as research areas where the theoretical framework provided by the theory was rendered functional. In recent years, the use of theoretical frameworks has become a necessity to assess the changing needs of the study of nursing science. The method used in the study can be effectively used to analyze the conceptual structures in the nursing education and professional application processes and to understand the origins of these theories. Further research can help to structure the use of informational science-based research designs to understand the connections between theory, clinical practice, and the development of educational contents in nursing faculties.

Selfies and Speeches of a President at War: Volodymyr Zelensky’s Strategy of Spectacularization on Instagram

The spectacularization of politics is not the exclusive patrimony of the media in their news coverage. Leaders from all over the world have successfully incorporated this “communicative style” into their own strategies, a style that finds a suitable space in visual social networks such as Instagram, in dynamic formats such as video, and in crisis contexts. This article analyzes the “spectacularization” of Ukrainian President Volodymyr Zelensky’s through Instagram. The methodological proposal is based on the study of the 5W in relation to the digital image and investigates the leading role, the staging, the space, and the technical resources of the videos. Thus, a content analysis is applied to a sample of 143 videos published by the president on his official profile in February–March 2022, when the conflict between Russia and Ukraine began, and in September–October 2022. We observed that Zelensky uses video in an intense and “spectacular” way: He exploits his figure in a professional style, strategically combines careful staging with amateurism, and uses resources such as subtitles to internationalize his discourse.