Social Network Analysis Techniques Research Articles

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

Spatial association of coastal towns and villages in Funing District, Qinhuangdao: A study using social network analysis

This study aims to explore how the spatial network structure between towns and villages can be optimized, which is a crucial element of rural revitalization strategies. For this investigation, we choose Funing District in Qinhuangdao, situated along the coastal region of the Bohai Sea. To achieve this, a gravity model is constructed through integration and collection of data, then applied social network analysis (SNA) techniques. Empirical analysis is conducted using ArcGIS and the NetworkX library in Python to uncover essential aspects of the town-village network in Funing District, including overall network density, centrality, cohesive subgroups, and core-periphery structures. The results of this research reveal a core-periphery structure in the town-village network of Funing District, highlighting significant hierarchical differences among nodes regarding spatial connectivity. With these findings in mind, specific measures are proposed to improve the flow of elements between nodes and optimize the spatial network structure. These measures encompass enhancing the strategic position of urban nodes, endorsing rural revitalization initiatives, and actively advocating for the establishment of a spatial layout embodying "one core, three stars, four guards, one axis and one belt" to facilitate resources and elements integration between urban and rural locales. Furthermore, this study offers valuable insights and references for town-village planning and development across the Bohai Rim region, encompassing the Beijing-Tianjin-Hebei area and analogous regions.

Art Crime Does not pay: Multiplexed Social Network Analysis in Cultural Heritage Trafficking Forensics

Nowadays, crimes connected to cultural heritage can feature as a staple for organised crime networks and act as financial enablers for international conflicts, including terrorism organisations and even inter-state conflicts, in several ways. Goods of cultural significance include a range of valuable objects related to human cultures, like works of art, historical artefacts, and other antiques, but also forgeries based on such objects. These crimes are almost always transnational, for instance, involving theft or looting in one country and goods moved across borders to be sold. This article presents an intelligence methodology based on Social Network Analysis (SNA) techniques that can support law enforcement agencies (LEAs) in their daily struggle against criminals that also pose a threat to national security. The methodology proposed is based on the building of a blended, multiplexed social network graph, deriving from the fusion of a diverse set of data sources, both in the open-source domain (OSINT) and in the classified domain. We will present data collection methods, correlation between sources, possible ways to generate blended links between individuals that retain information from different sources, and SNA techniques applied to intelligence and investigations. The article provides an answer to the following research questions: how we can detect and identify criminal activities and networks related to cultural goods crimes, how we can assist LEAs in countering illicit trafficking, and how we can ensure that art crime does not pay.

Research on Mixed Education Teaching Reform in the Era of Internet plus

Nowadays, the technology of "Internet plus" is developing very rapidly. Its cooperation with education promotes the innovative development of educational reform practice, improves the vitality of education. The cooperation of online and offline education has innovated teaching ideas and changed the teaching situation. It fully enhances students' proactive practical skills, thereby improving their comprehensive quality level, developing new educational concepts, and promoting innovation in new teaching models. Tourism behavior prediction based on social network analysis employs social network analysis techniques to predict the behavior patterns of tourists in different stages of their travel, such as destination selection, itinerary planning, and activity participation. By analyzing the behavior patterns of tourists on social media platforms, social network analysis algorithms can identify the factors that influence their decision-making, such as personal preferences, social influence, and situational context, and generate personalized recommendations to improve their travel experience. For example, if a tourist has shown a preference for outdoor activities on social media, tourism behavior prediction systems can recommend relevant activities and destinations that match their interests. These systems can also incorporate real-time data, such as weather forecasts and traffic conditions, to adjust recommendations accordingly. Overall, tourism behavior prediction based on social network analysis can provide tourists with more personalized and satisfactory travel experiences, while also promoting the development of local tourism industries.

Exploring the backward and forward linkages of production network in a developing country

This paper applies social network analysis (SNA) techniques to the input–output table in order to investigate the production network of Pakistan’s economy. Through different network measures, it identifies the sectors that exert significant direct and indirect impacts on other sectors. The findings reveal that, overall, sectors have weak forward linkages relative to backward linkages. The services sectors, particularly transportation and trade services, have the highest out-degree (a measure of forward linkages). In contrast, the manufacturing sectors demonstrate comparatively lower connectivity than the services sectors. The sectors of electricity, petroleum, and chemicals emerge as the most widely utilized inputs across various industries. Despite their relatively moderate immediate absorption in downstream industries, these sectors possess the potential to affect other industries indirectly. Considering the combined measures of network analysis, these sectors also emerge as the most crucial sectors in Pakistan’s production network. This suggests that enhancing efficiency in the energy, refining, and petrochemical industries will yield the highest economy-wide impact.

Integrating tourists’ walk and talk: a methodological approach for tracking and analysing tourists’ real behaviours for more sustainable destinations

This study provides a methodological contribution in the analysis of tourists’ sustainable behaviour. Traditional methods encounter challenges in addressing the attitude-behaviour gap, prompting the need for new approaches. Using diverse big data sources, the paper introduces a novel methodology for analysing real behaviour patterns, extending Social Network Analysis techniques. The aim is to identify sustainable behaviours that ease overcrowding, distribute visitor flows, and optimize economic diversification. The methodology is valuable for local authorities in monitoring tourist behaviours, aiding in informed decision-making for a more sustainable approach to tourism.

Deconstructing cultural appropriation in online communities: A multilayer network analysis approach

In this study, we introduce a novel multilayer network model designed to analyze complex social phenomena in online communities. The model captures intricate relationships between users, content, and specific aspects of social phenomena, providing a comprehensive framework for understanding these interactions. We applied this model to a dataset of over 1 million Reddit comments from January to April 2022, filtered for cultural appropriation-related keywords. Our quantitative analyses, based on Social Network Analysis techniques, revealed significant findings. For instance, a subreddit exhibited the highest user interaction, indicating a substantial level of engagement on this topic. Furthermore, the distribution of key contents across different subreddits was non-uniform, suggesting diverse levels of engagement across communities. The results of this research underscore the potential of our approach in providing a nuanced understanding of social phenomena in online communities, thereby contributing to future research in this field.

A network analysis-based framework to understand the representation dynamics of graph neural networks

In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.

Social Network Analysis as a Driver of Continuous Improvement

Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.

Corporate Networks and Business Groups in Egypt, 1924–1948: Economic Necessity or Entrepreneurial Dynamism

By providing fresh insights into the development of Egyptian corporate networks and business groups between the early 1920s and late 1940s, this article extends the geographical ambit of an expanding field of study. Data from contemporaneous sources were analyzed through social network analysis techniques, in the search for an improved understanding of both the nature of and motivations behind networking in Egypt. In conducting this exercise, this article assesses the success with which the state acted to stimulate local entrepreneurship in its search for greater economic independence. By linking the Egyptian case to a wider literature on corporate networks, the article reveals similarities with other cases of how developing economies responded to the challenges of that era.

Interactive mapping of Covid-19 disinformation in Ibero-America

Fake news, created with different intentions and disseminated through various social networks and messaging platforms, has become a significant means of disinformation in the current landscape. In the face of certain events or controversial situations, the number of fake new items increases, a situation that can become worrying. One such event was the arrival of the Covid-19 pandemic. To combat this phenomenon, various fact-checking bodies appeared with the objective of demonstrating and disseminating whether a certain piece of information was correct. At the international level, these organizations have joined together in the #CoronaVirusFacts/#DatosCoronaVirus Alliance. The organization Chequeado from Argentina has collected information coming from Latin American (Latam) countries plus Spain and Portugal in a specific database, which allows for their analysis on an independent basis. The objective of this work is to analyze and make graphically visible the thematic distribution, media typology, and digital formats of the fake news that circulated regarding the coronavirus pandemic in Ibero-America by means of the social network analysis technique (SNA) and others, using the information from the aforementioned database. Its final visualization is shown by means of interactive mapping, which can be manipulated by the user to analyze the results through the application of different integrated filtering strategies.

Hate Speech in The Context of Digital Violence in Türkiye: Perceptions and Biases on Afghan Identity

According to the data of the Presidency of Migration Management (2021), the largest group of irregular migrants detected in Türkiye in 2021 were Afghans. The arrival of irregular migrants from Afghanistan to Türkiye alongside the Syrian refugees have caused a negatively charged reaction in Turkish society and from the main parties in the opposition. Based on this apparent reaction against the arrival of Afghan
 refugees, the claim can be made that Afghans are now the new “other” category in Türkiye. We identify two hashtags used on Twitter to protest the arrival of Afghans, which are #sınırnamustur (border is honor) and #hudutnamustur (frontier is honor) having the same meaning in Turkish and are Trend Topics on Twitter. The purpose of this study is to examine the reason why there are two hashtags on Twitter to protest the arrival of Afghan immigrants, what the differences and similarities are between
 the two in terms of how Afghans are defined and what kinds of hate speech they are subjected to. For this purpose, we applied Craswell’s mixed model in this study using both content analysis and social network analysis techniques. The investigation results reveal that the hashtag #sınırnamustur is a manifestation of a political endeavor, asserting that Afghan refugees are associated with a ‘security challenge’. In contrast, the hashtag #hudutnamustur represents a response from the general populace, embodying elements of cultural superiority.

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.

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques.

To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Hubs of embodied business services in a GVC world

This paper explores the phenomenon of functional upgrading in Global Value Chains (GVCs) by applying empirical techniques of Social Network Analysis and graph theory to data from the TiVA database for the period from 1995 to 2018. Specifically, we focus on two (from less to more complex) stages of GVC participation: the global supply of manufacturing inputs and the global supply of business services to be embodied in other countries’ exports. We identify which countries are the leading specialized global providers in each stage, analysing their evolutions over time and the relationship between both progressions. It highlights the trajectory of China that has increased its role as a hub in both stages to reach top positions. Using a dynamic panel data model, we find that the role of a country as a specialized global supplier of manufacturing inputs is associate with becoming a prominent global supplier of embodied business services, suggesting a path to functional upgrading in GVCs.

국내 관광산업 생태계 네트워크 분석

The tourism industry is undergoing rapid structural changes due to economic, technological, and social transformations. This study aims to identify the structure and characteristics of the domestic tourism business ecosystem using corporate data. Employing the business ecosystem theory and social network analysis techniques, the influence of key companies and the connectivity between companies are examined. The findings reveal several important insights. First, the analysis of connection centrality indicates that the travel business and the Korea Tourism Organization are core companies that engage in extensive interactions within the ecosystem. Contrary to previous studies categorizing public institutions as secondary stakeholders, this research demonstrates their high influence as core companies within the tourism industry. Second, proximity and centrality of prestige analyses highlight that companies involved in aviation and passenger transport hold top ranks. These transportation-related companies exert rapid and significant ripple effects within the ecosystem. Furthermore, the study identifies the tertiary supply chain through ego-network analysis of key companies such as Korean Air, Hana Tour, and Hotel Lotte. It reveals the involvement of various industries beyond tourism, including system and application software development, supply chain management, and telecommunication sales. This empirical insight provides a glimpse into the digital transformation occurring within the tourism industry. Based on these results, the study puts forth academic and policy implications for the development of a healthy tourism business ecosystem. Understanding the flow within the tourism industry and its interactions with related industries is essential for adapting to ongoing economic and technological changes, thereby fostering a sustainable and competitive tourism sector.

Fixing the meaning of floating signifier: Discourses and network analysis in the bioeconomy policy processes in Argentina and Uruguay

The concept of bioeconomy has spread globally as a floating signifier leading to multiple policies and strategies. As an ambiguous concept, bioeconomy policy agendas at national levels draw on a variety of discourses and visions later reshaped by the particular political and social contexts of each country or region. From the broad boundaries of the overarching bioeconomy meta-discourse, local dominant discourses emerge, created and transformed by international and national social actors in the frame of power relations displaying discursive struggles. Analyzing this body of social dimensions, particularly from less studied Global South countries, can contribute to a critical comprehension of ongoing bioeconomy policy processes and the relationship between international regimes and domestic policies. In recent years, efforts to develop bioeconomy and forest-based bioeconomy strategies and related policy-making were observed in Argentina and Uruguay, two neighbouring countries with analogous forest sector development. Against this background, our study aims to explore these ongoing forest-based bioeconomy policy processes. In order to do so, discourse analysis tools were used to identify current dominant visions regarding bioeconomy. Additionally, the main social agents dealing with and promoting forest-based bioeconomy as well as discourse coalitions were identified with social network analysis techniques. Our findings revealed generally scarce activity of bioeconomy policy debate in media, conducted by a narrow number of legitimatized actors, namely the private sector and interest groups in Argentina and government agencies and TNCs in Uruguay. Local promoters and dominant visions mirror pre-existing narratives and experiences excluding alternative visions. Uruguay's forest-based bioeconomy aims to mirror European experiences, while the Argentinian is embedded in the national agricultural bioeconomy discourse. As bioeconomy operates as a floating signifier, political demands are articulated to a particular meaningful configuration partially fixing bioeconomy's meaning. In this process, central actors exert structural power, indicated by their discourse network centrality as a parameter of their social capital, but also hegemonize bioeconomy excluding alternative actors and their visions.

Towards socio-digital rural territories to drive digital transformation: General conceptualisation and application to the olive areas of Andalusia, Spain

The future of rural areas, in different geographical contexts around the world, faces enormous challenges and uncertainties, with the emergence of complex territorial dynamics. One of the main disruptive factors underlying the current challenges in rural realities is digital transformation (DT). This study has two objectives: 1) to develop a theoretical framework for the analysis of DT in rural areas from a territorial perspective, through the proposal of the concept of ‘socio-digital rural territories’ (SDRT); and 2) to analyse the specific case of the olive areas of Andalusia where DT is starting in recent years. For this purpose, Social Network Analysis (SNA) techniques are used, both to build the theoretical framework and to evaluate the concepts developed at a practical level in the case study. The results obtained show that addressing DT in rural areas from a territorial approach should consider the technological innovation system (TIS) and relational social capital (RSC), embedded in the technological and social dimensions of a territory, as key elements to promote successful dynamics towards a SDRT. On the other hand, the analysis of the DT network in the olive-growing areas of Andalusia (the DT olive network) shows that the actors that stand out for their role as knowledge emitters include digital technologies companies, knowledge generation agents, knowledge transfer agents and the scientific and informative media. On the contrary, the main receiving knowledge actors are olive growers, cooperatives or cooperative groups, non-cooperative groups and agricultural organisations. The density of connections in the network is high considering the type of actors and connections analysed. The DT olive network has a high potential for the creation of RSC. Thus, there is bonding capital, in which a high number of connections between actors predominate, although with low homogeneity in the typology of actors. And there is bridging capital, in which organisations of different types participate, and as it is a decentralised network, it favours the lack of concentration of power. Finally, this article offers a novel explanation of how knowledge is exchanged and created in a social network, and how this exchange creates new intellectual capital. This capital, which in the case of DT is territorial digital capital, is translated into digital technologies. The results indicate that the DT olive network and its RSC lead to the potential formation of an SDRT in Andalusian olive areas.

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.

From Korea to the world: women’s role as peer-leaders in K-pop transnational online brand communities

ABSTRACT Focusing on online brand communities (OBCs) centred around K-pop music consumption, this study explores the influential role of women as peer-leaders. By analysing data from Twitter using social network analysis (SNA) and sentiment analysis techniques, we observe that (1) OBCs naturally form transnational consumer segments, (2) where peer leadership emerges organically, and (3) women often occupy these leadership positions due to their social capital and engagement in relevant conversation topics. This study contributes to demonstrating the increasing empowerment of women in a realm that has long been criticized for its portrayal of gender-related issues.