Social Network Visualization Research Articles

Multimodal critical discourse analysis of the cinematic representation of women as social actors

ABSTRACT In this paper, an expanded version of the visual social actor network (van Leeuwen 2008) is suggested in integration with the classification of fields proposed by Martin (1992). In the context of multimodal critical discourse analysis, such network has provided a template for the discussion of the data aiming to reveal and interrogate the construal of women’s agency in the introductory part of the first film Sex and the City (2008). Overall results place women’s actions mostly in the private sphere but also reveal instances of gender performativity (Butler 1990) that question social norms for women mainly imposed by the media.

Silent No More: Women as Significant Political Intermediaries in Ottoman Algeria

Over the course of nearly 300 years, Ottoman and non-Ottoman governors struggled to suppress the recalcitrant clans of the eastern province of Algeria and sustain control. Algerian women proved invaluable partners in these efforts. After the mid-seventeenth century, most Ottoman officials married into a local family as one of the surest ways to establish their legitimacy among the Algerian elite. Through text mining to extract named and unnamed entities and social network visualization to illustrate their relationships, I represent unnamed women’s spectral presence despite their absence in the archival record. These kinship connections and the sub-communities to which they give rise can be meaningfully investigated quantitatively using social network analysis measures, such as betweenness centrality scores. Examining these quantitative measures reveals both named and unnamed women’s positions within the structure of Ottoman-Algerian society. Through an analysis of the individual lives, relationships and the underlying structure that make up the Ottoman-Algerian network in Constantine between 1567 and 1837, I argue that local women were the most significant links in the chain that bound Algeria to the Ottoman Empire.

Social network analysis of COVID-19 transmission in Karnataka, India.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

Education Policy Networks: The Co-Optation, Coordination, and Commodification of the School-to-Prison Pipeline Critique

This study utilizes social network visualization and content analysis of policy reports to explore the changing structure and function of a network of organizations, including government agencies, philanthropies, think tanks, advocacy groups, research centers, and edu-businesses active in school discipline policy formation at the federal level from 2000 to 2014. This study illuminates the mechanism by which the state, working through a complex public-private network of education policy actors, co-opts more radical demands to address the school-to-prison pipeline, coordinates interests across a range of social forces with particular attention to law enforcement, and commodifies the school-to-prison pipeline critique into marketable products and services for schools and school districts.

Marketing on Instagram

Purpose Social network sites are becoming more visual-centric. The purpose of this paper is to examine the effectiveness of visual social network sites as a marketing platform. Design/methodology/approach This paper comprises two experiments. The first experiment sought to examine the effect of social influence; while the second experiment examined the effect of image quality on the perception of quality and purchase intention. Findings The first experiment showed that social influence did not affect perceived quality or purchase intention. In the second experiment, a one-way between-groups multivariate analysis of covariance (MANCOVA) adjusting for sport involvement showed a statistically significant difference between the two groups. Respondents in the experimental group reported higher levels of perceived quality (adjusted M=3.68) and purchase intention (adjusted M=3.23) when compared to the control group’s perceived quality (adjusted M=3.12) and purchase intention (adjusted M=2.17). Research limitations/implications One of the limitations of this study is that it only examined the effects through a single social network site, i.e. Instagram. As there are other visual-centric social network sites, such as Tumblr and Pinterest, which operate slightly different from Instagram, it remains to be established how such effects vary across these social network sites. Practical implications The results suggest that social influence on Instagram is limited and marketers should invest in images of high quality when marketing on visual social network sites. Originality/value While there are many studies examining the effectiveness of marketing on social network sites, these studies have primarily focussed on earlier social network sites, such as Facebook. Newer social network sites that are more visual-centric, such as Instagram, are different from earlier social network sites and studied to a lesser extent. This study adds insights on the marketing effectiveness of visual-centric social network sites and deepens the understanding on marketing in general on social media.

Assessing the potential of social enterprises through social network analysis - Evidence from Albania

Organizations falling within the category of ‘social enterprises’ are rapidly expanding in transition countries, representing an innovative instrument for poverty reduction and endogenous development. However, economic long-term sustainability remains a major problem. The acquisition of knowledge is then claimed as a key issue for their competiveness and survival in the market. A critical factor for implementing successful knowledge transfer relates to the creation of well-connected networks of relationships. Stable and collaborative contacts also allow more effective coordination, reducing the risk of dispersed and fragmented initiatives. This paper investigates, by means of social network analysis, the Albanian social enterprise sector, focusing on two network relationships: social enterprises’ networks (i.e. inter-social enterprise relationships) and supporting actors’ networks (i.e. relationships amongst social enterprises and other actors). The results are based on empirical evidence collected at the firm level from organizations operating in the Tirana and Scutari regions. Visual social network investigation, along with networks’ correlation analysis, have shown that Albanian social enterprises are poorly aware of advantages arising from their network of relationships, still showing low entrepreneurial attitudes.

Visual Sentiment Analysis by Combining Global and Local Information

With the development of visual social networks, the sentiment analysis of images has quickly emerged for opinion mining. Based on the observation that the sentiments conveyed by some images are related to salient objects in them, we propose a scheme for visual sentiment analysis that combines global and local information. First, the sentiment is predicted from the entire images. Second, it is judged whether there are salient objects in an image or not. If there are, sub-images are cropped from the entire image based on the detection window of the salient objects. Moreover, a CNN model is trained for the set of sub-images. Predictions of sentiments from entire images and sub-images are then fused together to obtain the final results. If no salient object is detected in the images, the sentiment predicted directly from entire images is used as the final result. The compared experimental results show that the proposed approach is superior to state-of-the-art algorithms. It also demonstrates that reasonably utilizing the local information could improve the performance for visual sentiment analysis.

Methodological research on partial least squares structural equation modeling (PLS-SEM)

PurposeThe purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field.Design/methodology/approachBased on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions.FindingsThe analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research.Originality/valueAddressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

VizTract: Visualization of Complex Social Networks for Easy User Perception

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.

Identifying potential change leaders through social network visualizations

Every organizational change is considered a big challenge. Even having assured enough of the resources needed to drive the change successfully, there is also the necessity to choose the people who would be able to properly lead the change. Organizational network analysis provides some techniques and methods that help in visualizing the informal organizational structure. In the following paper some of these will be presented with the emphasis on showing the potentially key persons for the change that is planned to be developed. In the presented case study one large company was examined. The aim of the article is to analyse the problem of choosing the right people to drive change. In order to select potential change leaders there is an algorithm proposed which takes into consideration two aspects. Namely, the intensity of the archetypical leadership value of the actor, and the actor’s position in the informal network. The results confirm that a relatively small group of change leaders can directly reach the majority of employees, which is one of the crucial factors for the change to succeed. The visualizations used in the study can shorten the time needed to find the right people to drive the change, and also reduce the probability of wrong guesses provided by the intuition.

Exploring Patterns of Social Relationships among Food Bloggers on Twitter Using a Social Network Analysis Approach

Abstract Background and objective . Nutrition information conveyed by popular entities through online social networking sites (i.e., social media influencers) has the potential to impact consumer eating behavior through mechanisms of social influence. Little is known about how online communities of food-related social media influencers are structured, which could reveal influencers’ opportunities to observe and spread nutrition-related content and information design practices. This study explored patterns of social relationships (social capital, conservation of resources, and homophily) within a network of prominent food bloggers on Twitter (N = 44). Methods . Data on Twitter following/follower relationships and Twitter use (number of tweets, favorited tweets) were collected from bloggers’ Twitter profiles. Bloggers represented eight topical subcategories of food blogs (e.g., family cooking, cocktails) and comprised a one-mode social network with directed ties indicating Twitter following/follower relationships. Structural evidence of patterns of social relationships was investigated through social network visualization, centrality measures (in-degree/out-degree centrality, density, reciprocity), and inferential tests. Results . The overall network density of directed ties was 21%, with wide variability in individual blogger centrality across multiple measures. Cocktails, cooking, special diets, and culinary travel bloggers had more dense ties to bloggers in their own subcategories. Within the network, favorited tweets and outreach (Twitter following relationships) were positively associated with popularity (Twitter follower relationships). Conclusions . Food bloggers in this study formed a partially connected network, supporting the conservation of resources framework. Homophily was evident in some, but not all, topical subcategories. Associations among Twitter use, outreach, and popularity generally supported the social capital framework. Future studies should explore influencers’ motivations for connecting on social networking sites, and how content and information design practices spread among influencers.

Visualized Social Network Analysis on Spatial Dynamics of International Trade between China and League of Arab States

China and Arabian countries keep profound trade relations along the history and at recent decays. In confronting with the dramatic change of geopolitical context nowadays, it will be urgent for Chinese decision makers to exam the opportunities and/or risks of regional business preferences in the future through probing into the spatial dynamics of inter-trade with Arab members in the past years. By applying the social network analysis method and visualization tools of ArcGIS, the paper processes the bilateral trade data of related countries during 2001 and 2017. The initial findings show that about 84% percent of inter-trade volume comes from West Asian Arab members along the Persian Gulf; China is getting closer in trade relations with major LAS partners than LAS members themselves during the period, implying the logic of relational versus geographical proximity in between. Further research should be carried out in tackling with the forces and their mechanism that driving the trade spatial differentiation.

The social fabric of citizen science\u2014drivers for long-term engagement in the German butterfly monitoring scheme

Insect conservation needs sound information on species distribution trends. Developing this evidence relies—in practice—on long-term engagement of volunteers who observe and record species over large spatial and temporal scales. Many biodiversity monitoring schemes, including those for insects, are highly dependent on conservation-based citizen science programs with a long-term continuity. As these schemes are built entirely on good will, the nature of social relations and networks is pivotal to success. We assess the working mechanism of a monitoring scheme that is citizen-based as a case study. The German Butterfly Monitoring Scheme (hereafter TMD for “Tagfalter-Monitoring Deutschland”) operates, as many other citizen science monitoring schemes, through an overarching national network of regional subnetworks of volunteers and a central scientific coordination. Using a questionnaire survey paired with a visual social network assessment, we investigate how participants interact within these networks and assess their motivations to engage. We characterise the functionality of this social network based on mechanism of coordination and participation, flows of information and knowledge exchange among recorders, regional and central coordinators, academic scientists and institutions. By analyzing the interactions, we show how the social network facilitates and ensures various communication modes and thereby fosters long-term engagement, stability and growth of the scheme. We identify the central role of project coordination and the importance of social relations within citizen-based monitoring programs for engagement and personal satisfaction. Based on our empirical study, we derive a set of recommendations for establishing and maintaining successful volunteer networks in insect citizen-based monitoring programs.

Analyzing students\u2019 collaboration patterns in a social learning environment using StudentViz platform

Understanding students’ collaboration patterns is an important goal for teachers, who can thus obtain an insight into the collaborative learning process. Social network analysis and network visualizations are commonly used for exploring social interactions between learners. However, most existing network visualization platforms are deemed too complex by the teachers, who do not possess social network analysis expertise. Therefore, we propose an easy to use platform for visualizing students’ collaboration patterns, called StudentViz. An overview of the tool, including a description of its implementation and functionalities, is provided in the paper. An illustration of how the tool can be used in practice, for investigating students’ collaboration patterns in a social learning environment, is also included.

Soft computing model based financial aware spatiotemporal social network analysis and visualization for smart cities

The era of intelligence is the development of human science and technology at a higher level, bringing a new layout for the financial market. Then, how to realize the good layout of the financial market in the era of intelligence is an important problem facing all the countries in the world. We in the financial industry as the origin and change as the clue, analyzes the physical outlets as the representative of the financial institutions, banking as standard electronic banking and mobile phone to the bank as the representative of the mobile financial development of three major formats. On this basis, we analyze the current mobile phone terminal model of mobile banking three shortcomings, and propose the new mobile financial formats. This new format is tentatively known as “smart financial format”, it has the equipment personality, wearable, low carbon environmental protection, offline interaction, security, privacy and intelligence, efficiency, and other five major characteristics. In the future, the financial format is likely to be achieved by mobile financial formats to the upgrading of intelligent financial formats, to meet the needs of customers wider and deeper levels. The artificial intelligence method has its advantages in dealing with the problems of the economic system. Therefore, the introduction of artificial intelligence methods into the economic control will become a trend. The proposed model is validated through the public databases to verify the effectiveness.

Feasibility of implementing an electronic social support and resource visualization tool for caregivers in a neuro-oncology clinic.

The goals of this study were to assess the feasibility of a web-based application-electronic Social Network Assessment Program (eSNAP)-to automate the capture and visualization of family caregiver social network data of neuro-oncology patients. Caregivers were recruited from a neuro-oncology clinic at an NCI-designated comprehensive cancer center. Participants completed baseline questionnaires on a laptop in clinic assessing demographic characteristics. After baseline, participants were randomly assigned to either create a social network visualization using eSNAP (intervention) or to usual care (control) condition. Those who used eSNAP provided likeability/usability data. All participants were asked to complete follow-up questionnaires at 3 and 6weeks after baseline to determine feasibility of longitudinal study. We recruited 40 caregivers of patients with primary malignant brain tumor to participate in this study. Participants rated eSNAP usability and likeability highly, indicating that eSNAP would help them consider their available social support. At 3weeks, 90% of participants completed questionnaires and 82.5% completed questionnaires at 6weeks. There is a need to encourage family caregivers of patients with primary malignant brain tumor to engage their existing social network resources to help alleviate caregiver burden. Our findings suggest that our web-based application to address this issue is feasible to implement with high usability and likeability. This pilot study identified minor changes to the intervention to improve effectiveness and has implications for future research in this understudied population. clinicaltrials.gov, protocol number NCT03026699.

Learning in the wild

The theoretical lenses, empirical measures and analytical tools associated with social network analysis comprise a wealth of knowledge that can be used to analyse networked learning. This has popularized the use of the social network analysis approach to understand and visualize structures and dynamics in online learning networks, particularly where data could be automatically gathered and analysed. Research in the field of social network learning analysis has (a) used social network visualizations as a feedback mechanism and an intervention to enhance online social learning activities (Bakharia & Dawson, 2011; Schreurs, Teplovs, Ferguson, de Laat, & Buckingham Shum, 2013), (b) investigated what variables predicted the formation of learning ties in networked learning processes (Cho, Gay, Davidson, & Ingraffea, 2007), (c) predicted learning outcomes in online environments (Russo & Koesten, 2005), and (d) studied the nature of the learning ties (de Laat, 2006). This paper expands the understanding of the variables predicting the formation of learning ties in online informal environments. Reddit, an online news sharing site that is commonly referred to as ‘the front page of the Internet’, has been chosen as the environment for our investigation because conversations on it emerge from the contributions of members, and it combines perspectives of experts and non-experts (Moore & Chuang, 2017) taking place in a plethora of subcultures (subreddits) occurring outside traditional settings. We study two subreddit communities, ‘AskStatistics’, and ‘AskSocialScience’, in which we believe that informal learning is likely to happen in Reddit, and which offer avenues for comparison both in terms of the communication dynamics and learning processes occurring between members. We gathered all the interactions amongst the users of these two subreddit communities for a 1-year period, from January 1st, 2015 until December 31st, 2015. Exponential Random Graph models (ERGm) were employed to determine the endogenous (network) and exogenous (node attributes) factors facilitating the networked ties amongst the users of these communities. We found evidence that Redditors’ networked ties arise from network dynamics (reciprocity and transitivity) and from the Redditors’ role as a moderator in the subreddit communities. These results shed light into the understanding of the variables predicting the formation of ties in informal networked learning environments, and more broadly contribute to the development of the field of social network learning analysis.

Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks

Research on group cohesion often relies on individual perceptions, which may not reflect the actual social structure of groups. This study draws on social network theory to examine the relationship between observable structural group characteristics and individual perceptions of group cohesion. Leveraging Facebook data, we extracted and partitioned the social networks of 109 participants into groups using a modularity algorithm. We then surveyed perceptions of cohesion, and computed group density and size using social network analysis. Out of six linear mixed effects models specified, a random intercept and fixed slope model with group size as a predictor of perceived group cohesion emerged as best fitting. Whereas group density was not linked to perceived cohesion, size had a small negative effect on perceived cohesion, suggesting that people perceive smaller groups as more cohesive. We discuss the potential of social network analysis, visualization tools, and Facebook data for advancing research on groups.

The Role of Consumer Affect on Visual Social Networking Sites: How Consumers Build Brand Relationships

ABSTRACTVisual social networking sites, such as Instagram, have become increasingly attractive for engaging consumers and building brand relationships. Despite this growing popularity, little is known about how visuals help consumers build brand relationships. Therefore, this study examines how consumers' perceptions of brand engagement on Instagram influence their affective response and build brand commitment with regard to consumers' three-dimensional emotional response—appeal, engagement, and empowerment. Findings suggest that consumers' emotional responses each contribute to consumers' brand commitment. Additionally, perceived entertainment and aesthetic values of brand posts were positively related to appeal; perceived belongingness to brands with engagement; and perceived mutuality with empowerment.

Scientific Collaboration Networks of Mathematicians from the Former Soviet Union in the Global South

Scientific Collaboration Networks of Mathematicians from the Former Soviet Union in the Global South Isabel Izquierdo, Hebe Vessuri, Rosa Luz Gonzalez Abstract This article explores the scientific collaboration networks of mathematicians from the Former USSR in the Global South, the case of Mexico. We used the Social Network Analysis (ARS) macro perspective focusing on the collaboration that scientists have had since they arrived in Mexico. We also use the public Database of the American Mathematical Society (MathScience) from which collaborations are reported in articles, chapters of books and proceedings published, and the Pajek software as tools for the analysis and visualization of social networks. The results of the study show the heterogeneity of scientific network collaboration nationally, regionally and internationally, and brings to debate some considerations that have not been taken into account when contexts and spaces do not correspond to traditional and binary “South-North” highly skilled migration Full Text: PDF DOI: 10.15640/jehd.v7n4a10