Social Network Analysis Tools Research Articles

Mapping epistemic pluralism: A network analysis of discursive practices in communities promoting refused knowledge about healthcare and wellbeing

This article presents an analysis of discourses performed in communities that share and disseminate knowledge refused by institutional science. The study focuses on an online community concerned with alkaline water, food, and lifestyle, aiming at understanding how promoters of refused knowledge in this community enrol other forms of knowledge, including science. Theoretically, this work is framed in Science & Technology Studies, and in the tradition of actor-network theory, situating itself in a recent turn that takes epistemic instability and pluralism into due consideration, thus overcoming opaque views of the opposition between science and non-science. Empirically, this fine-grained analytic purpose is addressed by a mixed-method strategy in which discursive practices are observed through a web-ethnography conducted between January 2020 and December 2021 on the relevant online spaces and then analysed qualitatively and quantitively by means of formal techniques. Relying on the tools of social network analysis, the discursive space of the community under study is formalised as a two-mode network of knowledge claims and heterogeneous actors enrolled in discourse to sustain those claims. Then, community detection is performed to map the different assemblages of claims and actors and the relevant repertoires characterising those assemblages. Finally, multiple correspondence analysis applied to two-mode networks is used to highlight the dimensions of concern and meaning expressed in the knowledge organisation of this community.

Discursive Practices during an Electoral Cycle: Public Opinion and Political Disillusionment on Twitter

Electoral cycles are highly charged, politically intense moments that influence public discourse. Political elites, citizens, and the traditional news media seek to generate opinions and interactions on social networks. This research is motivated by the following questions: What are the communicative and deliberative practices used in these spaces? Is it possible to identify the characteristics that –through deliberative conversations– potentially foster or undermine democratic debate, particularly when using populist and polarizing discourse? Using a mixed methods approach, we apply a social network analysis tool to track conversations and identify the volume of political discourse (N=346,000). Using selective and staged filtering, we identified posts from nine Peruvian Twitter accounts during the September 2022 electoral campaign in Lima: three candidates, three media outlets, and three accounts with high levels of engagement. Our data comprised the comments from these nine accounts and was extracted using an Application Programming Interface (API). Subsequently, we carried out ethnographic content analysis on publications with more than 30 comments. In this phase, we analyzed Twitter comments using a codebook to identify deliberative practices and user responses. Our findings underscore the significant role of principal media outlets in shaping political conversation on Twitter. We also discovered that attempts to interact and deliberate were often overshadowed by heated comments attempting to impose opinions on others. Most importantly, our research reveals a widespread disillusionment with politics, political institutions, and even the political preferences of fellow citizens in Peru, highlighting a key challenge for democratic discourse on social media.

Empowering Equality Law With Nlp: A Social Network Analysis Approach to Drive Legal SDGS

Objectives: The purpose of this study is to use social media analysis to understand people's opinions about legal equality. Theoretical Framework: The study builds on social studies theory and existing literature to explore the complexity of society's reaction to legal equality. It focuses on how public opinion evolves over time and the factors influencing these changes, including language, age, and gender. Method: Data was gathered from various social media platforms on September 8, 14, and 15, 2023, using the Talkwalker Social Intelligence Platform. The analysis involved monitoring public opinion trends and performing a theme analysis to identify recurring themes in the discourse. Results and Discussion: The findings revealed essential trends in societal reactions to legal equality. Notable changes in perceptions of specific issues were observed, and prominent aspects of legal equality, such as language, age, and gender, were identified. These results provide a comprehensive understanding of how society perceives legal equality and the dynamics influencing these perceptions. Research Implications: The study enhances existing literature and advances social studies theory by offering insights into the complexity of societal reactions to legal equality. It also demonstrates the potential of social media analysis using Social Network Analysis (SNA) and Talkwalker-type tools to understand how social interactions shape opinions and to inform policy planning. Originality/Value: This research illuminates the social dynamics of legal equality and showcases the effectiveness of combining SNA with social media analysis tools. The findings offer valuable contributions to theoretical and practical discussions on legal equality and policy-making, particularly in the context of promoting the SDGs.

Quantifying Women’s Marginalisation in Ibero-American Film Culture During the First Half of the Twentieth Century: A Network-Science Proposal

The research presented here uses the tools of social network analysis to empirically show a socio-cultural phenomenon already addressed by the social sciences and history: the historical marginalisation of women in the field of cinema. The novelty of our approach lies in the use of a large amount of heterogeneous historical data. On the one hand, we built a network of interactions between people involved in the film field in Ibero-America during the first half of the twentieth century. On the other hand, we propose a k-core decomposition and a multi-layered analysis, as a quantitative way to study the position of women within the cultural melieu. After conducting our analysis, we concluded that women were mostly situated in the outer k-shells of the empirical network, and their distribution was not uniform across the k-shells. From a qualitative perspective, these results can be interpreted as the consequence of the lack of evidence of the participation of women in the public sphere.

Piecing together ‘big pictures’ with social network analysis and digital tools

Abstract When considering the ‘big picture’ in the history of science, what or who is considered important depends upon the focal point of the analysis. Social network analysis, equipped with digital methods, offers historians a way to help generate alternative perspectives for analysis by revealing elusive patterns obscured by the apparent ‘centre/periphery’ dichotomy or ‘great-men’ narratives. The presented methods are focused on studying connections, relationships and structural characteristics in networks and can thus help bridge global and local perspectives and suggest new historical lines of argument. This point is illustrated with a case study of Polish oil prospectors working in Argentina in the 1880s, which problematizes the narrative of the formation of transnational networks based on this mineral resource. The article discusses the place of digital methods within historical research, arguing that good skills in the interpretation and communication of outputs produced through digital research methods, coupled with familiarity with their associated theories, strengths and weaknesses, are indispensable.

Social Network Analysis: A Survey on Process, Tools, and Application

Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research verticals of SNA, such as viral marketing, information diffusion, clustering, link prediction, and so on, to provide background knowledge and understanding. These studies still lack the SNA process, tools, and practical aspects in multidisciplinary applications. Inspired by these facts, we have discussed the background, process, tools, and application of SNA. First, we have presented a detailed description of the SNA process. Thereafter, we presented a comparative analysis of SNA tools and languages. Finally, we have discussed the various applications corresponding to SNA research verticals.

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies.

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

Low credibility URL sharing on Twitter during reporting linking rare blood clots with the Oxford/AstraZeneca COVID-19 vaccine.

The COVID-19 pandemic was accompanied by an "infodemic" of misinformation. Misleading narratives around the virus, its origin, and treatments have had serious implications for public health. In March 2021, concerns were raised about links between the Oxford/AstraZeneca (AZ) COVID-19 vaccine and recipients developing blood clots. This paper aims to identify whether this prompted any reaction in the diffusion of low-credibility COVID-19-relate information on Twitter. Twitter's application programming interface was used to collect data containing COVID-19-related keywords between 4th and 25th March 2021, a period centred on the peak of new coverage linking rare blood clots with the AZ vaccine. We analysed and visualised the data using temporal analysis and social network analysis tools. We subsequently analysed the data to determine the most influential users and domains in the propagation of low-credibility information about COVID-19 and the AZ vaccine. This research presents evidence that the peak of news coverage linking rare blood clots with the AZ vaccine correlated with an increased volume and proportion of low-credibility AZ-related content propagated on Twitter. However, no equivalent changes to the volume, propagation, or network structure for the full dataset of COVID-19-related information or misinformation were observed. The research identified RT.com as the most prolific creator of low-credibility COVID-19-related content. It also highlighted the crucial role of self-promotion in the successful propagation of low-credibility content on Twitter. The findings suggest that the simple approach adopted within the research to identify the most popular and influential sources of low-credibility content presents a valuable opportunity for public health authorities and social media platforms to develop bespoke strategies to counter the propagation of misinformation in the aftermath of a breaking news event.

Bridging Gaps in Collaboration Between Community Organizations and Hospital-Based Violence Treatment Centers Serving Transgender Sexual Assault Survivors.

Community and healthcare organizations have not historically collaborated effectively, leaving gaps in the continuum of care for survivors of sexual assault. These gaps are particularly acutely felt by transgender (trans) survivors, who experience additional barriers to care and face higher rates of sexual assault. To bridge these gaps and enhance the provision of comprehensive support for trans people, we developed an intersectoral network of trans-positive community and hospital-based organizations in Ontario, Canada. As part of a baseline evaluation of the network, we conducted a social network analysis to determine the extent and nature of collaboration between members within and across these two sectors. Using a validated social network analysis tool (PARTNER survey), data were collected from June 22 to July 22, 2021. The extent of collaboration was examined by relationship type: intrasectoral (same sector) and intersectoral (different sectors). The nature of collaboration was examined using relational scores (value: power, level of involvement, potential resource contribution; trust: reliability, mission congruence, openness to discussion). Fifty-four community organizations (65.9% of 82 invited) and 24 hospital-based violence treatment centers (64.9% of 37 invited) responded. The majority of collaborations were within, rather than across, the two sectors: of all 378 collaborations described, 70.9% (n = 268) were intrasectoral collaborations and 29.1% (n = 110) were intersectoral collaborations. Intersectoral relationships were characterized by lower scores for level of involvement, trust, reliability, and mission congruence than intrasectoral relationships, but higher scores for power. These findings were shared in a virtual consultation session of key stakeholders, in which some participants expressed "surprise" and concern for the lack of collaboration and character of relationships across sectors. Recommendations to increase intersectoral collaboration, which included intersectoral program planning and service design and supporting increased opportunities for intersectoral training and knowledge exchange, are presented.

Linking Up Bell Beakers in the Iberian Peninsula

Many studies in complexity theory employ agent-based models whose interactions can be expressed as networks. In such models, the pattern of interactions between actors is crucial, and the network topology that emerges from the raw data can be characterized through many metrics. One tool previously used in archaeology studies has the potential to deal with networks in social contexts at different scales of analysis: social network analysis (SNA). This discipline has been applied successfully in a wide range of archaeological problems, providing valuable insights and a different perspective. It also could be helpful to quantify concepts associated with social complexity, such as robustness or resilience. In this work, we propose some methodologic possibilities for consideration in the phase definition of the adaptive cycle model (ACM), using SNA tools. To illustrate the process, we will present a case study from the Copper Age in the Iberian Peninsula: the Bell Beaker phase.

Scene clusters, causes, spatial patterns and strategies in the cultural landscape heritage of Tang Poetry Road in Eastern Zhejiang based on text mining

The burgeoning field of digital humanities has provided important modern technological means for text mining in literary works. Chinese classical poetry, as a treasure in the world’s artistic treasury, holds significant value in recognizing the heritage of world culture. In this study, taking the 1589 Tang poems from the Tang Poetry Road in Eastern Zhejiang as an example, we constructed a research framework that explores the aesthetics of classical Chinese poetry landscapes and spatial imagery at the urban agglomeration scale by utilizing geographic and analytical tools such as Python programming, Gephi co-occurrence semantic networks, and GIS kernel density analysis. The framework exhibits three key innovations: (1) a text processing approach that treats individual characters as semantic units in ancient poetry texts, (2) a combined approach of Python programming techniques and Gephi visualization tool for social network analysis, and (3) a study focusing on the integration of textual and spatial aspects of literary landscape heritage corridors at the urban cluster scale. The constructed framework greatly enhances the efficiency and accuracy of Tang poetry text mining, it enables the extraction of natural and cultural landscape spatial imagery along the Tang Poetry Road, the construction of scene depictions, the identification of key regions within the scenes, and the derivation of location-specific strategies. This study broadens the scope of exploring the cultural heritage value of Tang poetry literature and provides practical guidance for the development of cross-regional heritage corridors.

Nacjonalistyczny internet. Rekonstrukcja medialnego ekosystemu polskiej skrajnej prawicy w internecie

Głównym celem artykułu jest próba rekonstrukcji polskiego medialnego ekosystemu skrajnej prawicy funkcjonującego w internecie. Punktem wyjścia było 15 stron internetowych najbardziej rozpoznawalnych polskich organizacji skrajnie prawicowych, które za pomocą narzędzia Hyphe poddano indeksacji, by zidentyfikować kolejne strony o tym samym profilu ideologicznym. W ten sposób znaleziono 312 stron, które następnie z wykorzystaniem metod i narzędzi do analizy sieci społecznych poddano dalszym analizom w poszukiwaniu odpowiedzi na następujące pytania badawcze: 1) jak gęsta jest sieć połączeń między poszczególnymi stronami; 2) jaka jest różnorodność i dominująca orientacja ideologiczna skrajnie prawicowego internetu; 3) jakie typy stron wykorzystywane są do dystrybucji skrajnie prawicowych treści? Wyniki badań wykazały, że skrajnie prawicowe strony internetowe są dość słabo usieciowione, jednak istnieje kilka serwisów pełniących funkcję liderów opinii kluczowych dla poszczególnych grup oraz dystrybutorów treści łączących różne środowiska. Jednocześnie mimo różnorodności ideologicznej dwa środowiska – radykalnych nacjonalistów i autonomicznych nacjonalistów są kluczowe w rekonstruowanym ekosystemie medialnym. O ile to różnego rodzaju serwisy odgrywające role serwisów medialnych pozostają ważne dla każdego ze środowisk, to jednocześnie każde środowisko wykorzystuje różne typy stron w nieco odmienny sposób. Nationalist Internet. Reconstructing the Media Ecosystem of the Polish Far Right on the Internet: The article’s main objective is to reconstruct the media ecosystem of the Polish extreme right on the Internet. The starting point was 15 websites of the most recognisable Polish far-right organisations, which were indexed using the Hyphe tool to identify other websites with the same ideological profile, this identified 312 websites. These were then further analysed using social network analysis methods and tools in order to answer the following research questions: 1) how dense is the network of connections between the different sites; 2) what is the diversity and dominant ideological orientation of the far-right Internet; 3) what types of sites are used to disseminate far-right content? The research results show that far-right websites are poorly networked but that a few sites act as opinion leaders and content distributors linking different communities. At the same time, despite the ideological diversity, two communities – radical nationalists and autonomous nationalists – are critical in the reconstructed media ecosystem. While the different types of sites function as important media sites for each milieu, each milieu uses the different types of sites in slightly different ways.

Spatial network structure and driving factors of human settlements in three Northeastern provinces of China

IntroductionUrban human settlements' spatial network structures have emerged as crucial determinants impacting their health and sustainability. Understanding the influencing factors is pivotal for enhancing these settlements. This study focuses on 34 prefecture-level cities in Northeastern China from 2005 to 2020. It employs a modified gravitational model to establish spatial relationships among urban human settlements. Social network analysis techniques, including modularity and the quadratic assignment procedure (QAP) regression model, are introduced to analyze the network's characteristics and driving factors.MethodsA modified gravitational model is applied to create the spatial association network of urban human settlements. Social network analysis tools, along with modularity and the QAP regression model, are utilized to investigate the network's attributes and influencing elements. The study evaluates the evolution of spatial correlation, network cohesion, hierarchy, and efficiency.ResultsThroughout the study period, spatial correlation among urban human settlements in Northeastern China progressively intensified. However, the network exhibited relatively low density (0.217675), implying limited interconnectivity among cities. The average network hierarchy was 0.178225, indicating the need for optimization, while the average network efficiency was 0.714025, reflecting fewer redundant relationships. The analysis reveals the emergence of a polycentric network pattern with core and sub-core cities like Shenyang, Dalian, Changchun, Daqing, and Harbin. The urban network configuration has largely stabilized. The spatial association network showcases the intertwining of "small groups" and community organizations. Geographic proximity and merit-based linkages govern feature flow. Measures such as breaking administrative barriers, reducing flow time and distance, boosting resident income, and increasing government investment are identified to foster balanced network development and structural optimization.DiscussionThe research underscores the increasing spatial correlation and evolving network pattern among urban human settlements in Northeastern China. Despite the observed strengthening correlation, challenges related to network cohesion and hierarchy persist. The formation of a polycentric network signifies positive progress in urban development. The study highlights the importance of proximity and merit-based connections for feature flow. The proposed measures offer pathways to enhance network development and optimize structure, promoting holistic urban settlement growth and sustainability.

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

The emergence of the social networking phenomenon and the sudden spread of the coronavirus pandemic (COVID-19) around the world have significantly affected the transformation of the system of interpersonal relations, partly shifting them towards virtual reality. Online social networks have greatly expanded the boundaries of human interpersonal interaction and initiated processes of integration of different cultures. As a result, research into the possibilities of predicting human behavior through the characteristics of virtual communication in social networks has become more relevant. The aim of the study is: to explore the possibilities of machine learning model interpretability methods for interpreting the success of social network users based on their profile data. This paper uses a specific method of explainable artificial intelligence, SHAP (SHapley Additive exPlanations), to analyze and interpret trained machine learning models. The research is based on Social Network Analysis (SNA), a modern line of research conducted to understand different aspects of the social network as a whole as well as its individual nodes (users). User accounts on social networks provide detailed information that characterizes a user's personality, interests, and hobbies and reflects their current status. Characteristics of a personal profile also make it possible to identify social graphs - mathematical models reflecting the characteristics of interpersonal relationships of social network users. An important tool for social network analysis is various machine learning algorithms that make different predictions based on sets of characteristics (social network data). However, most of today's powerful machine learning methods are "black boxes," and therefore the challenge of interpreting and explaining their results arises. The study trained RandomForestClassifier and XGBClassifier models and showed the nature and degree of influence of the personal profile metrics of VKontakte social network users and indicators of their interpersonal relationship characteristics (graph metrics).

Storytelling With Networks: Realizing the Explanatory Potential of Network Diagrams Through the Integration of Qualitative Data

Network visualizations are alluring yet deceptively difficult to interpret. This article demonstrates several strategies for integrating qualitative data into interactive network diagrams for the purposes of providing contextual information about actors and their relationships. The result is a new joint display called a Narrated Network Diagram (NND). The development of NNDs aims to make network visualizations more reliable tools for reporting research findings, especially for researchers integrating Social Network Analysis tools into multi- and mixed-methods research designs. Until now, researchers primarily use interactivity to support exploratory analysis of large networks at various scales. However, interactivity can also be used to integrate diverse forms of data without sacrificing perceptual efficiency by essentially hiding significant amounts of text within nodes and edges as tooltips or by delineating additional data within legends, titles, and subtitles.

Forecasting consumer confidence through semantic network analysis of online news

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers’ judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

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.

Stakeholder engagement in coastal adaptation interventions: Insights from the Southwest coast of Nigeria

Coastal areas in developing nations are in a highly precarious situation, making adaptation an urgent policy option for the region. However, since the ultimate success of many implemented adaptation decisions may only be known once the evolving impacts of climate change test them, greater attention may be devoted to the decision-making processes that shape adaptation choices. This study, therefore, focused on the stakeholder engagement process, which as an integral component of the adaptation process, unifies the efforts, commitments, and knowledge of different individuals and groups toward the implementation and success of coastal adaptation projects. The study applied an integrated approach comprising Stakeholder Analysis and Social Network Analysis tools to identify, categorize, and map stakeholders' relationships on the Southwest coast of Nigeria. The result highlights the multiplicity of stakeholders across administrative levels. Furthermore, it shows the disconsolate interest and detachment of statutory stakeholders notwithstanding their powers while portraying the high degree of local relations (autonomous communities and trade associations). The result further highlights the widespread adoption of public consultation as the approach of choice for stakeholder engagement, notwithstanding its flaws. The study's implications are evident in the prevalence of autonomously undertaken adaptation strategies that are overly narrowly focused, disjointed, and counterproductive. Addressing this issue must start with formulating a coastal zone management policy and establishing a coastal zone management agency as the coordinating body for all coastal matters across the study area.

The risk of domino secessions: interdependent secessions and lessons from the Western Balkans

ABSTRACT The resurgence of nationalist tendencies around the world has brought new attention to the problem of smaller regions seceding from larger states. While research exists regarding the contagious nature of self-determination, ethnic conflict and civil wars, the interrelationships among various secessionist conflicts have not been systematically studied. We show that the circumstances of secession conflicts in different countries are interconnected by the perceptions of elites and that outcomes in one case may, therefore, influence expectations, motivations, strategies and outcomes in others. We also identified several indicators of interconnectivity among secessionist conflicts in the Western Balkans and visualised the strength of the links among them using social network analysis tools (Pajek – Spider). After the outbreak of a secession conflict, several inter-secession effects will be directly transferred to the most interconnected cases, and the greatest domino risk results from the combined impacts of direct and indirect nth-order effects.

Bank Risk Literature (1978–2022): A Bibliometric Analysis and Research Front Mapping

This study maps the conceptual structure of the body of knowledge concerning bank risk to understand this research strand better. A bibliometric analysis including 671 publications from January 1978 to October 2022 was conducted to achieve the aim of the study. The analysis of descriptive indicators identifies the main traits of scholars debating bank risk in terms of the annual production of publications; most productive authors, countries, affiliations, and journals; and most cited articles in the dataset. This study performs a co-word analysis by adopting social network analysis tools to analyze the conceptual structure of the dataset. The results highlight growing academic interest in bank risk research topics, especially following the global financial crisis. The bibliometric analysis reveals three main topics concerning the consideration of bank risk: (1) the adoption of risk management and bank risk, (2) the use of bank risk during the financial crisis, and (3) the interrelations between corporate governance and bank risk.