Social Network Analysis Tools Research Articles

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

Hiding in Temporal Networks

Social network analysis tools can infer various attributes just by scrutinizing one's connections. Several researchers have studied the problem faced by an evader whose goal is to strategically rewire their social connections in order to mislead such tools, thereby concealing their private attributes. However, to date this literature has only considered static networks, while neglecting the more general case of temporal networks, where the structure evolves over time. Driven by this observation, we study how the evader can conceal their importance from an adversary armed with temporal centrality measures. We consider computational and structural aspects of this problem: Is it computationally feasible to calculate optimal ways of hiding? If it is, what network characteristics facilitate hiding? This topic has been studied in static networks, but in this work, we add realism to the problem by considering temporal networks of edges changing in time. We find that it is usually computationally infeasible to find the optimal way of hiding. On the other hand, by manipulating one's contacts, one could add a surprising amount of privacy. Compared to static networks, temporal networks offer more strategies for this type of manipulation and are thus, to some extent, easier to hide in.

Analyzing the interorganizational collaborations in crisis management in coping with COVID-19 using social network analysis: Case of Iran.

On January 7, a novel coronavirus, originally abbreviated as 2019-nCoV by World Health Organization, was identified from the throat swab sample of a patient. The virus and its related disease (COVID-19) rapidly spread throughout the world and lead to a global pandemic. Iran as a developing country and one of the most populated countries in the area is dealing with the mentioned crisis. The main objective of the present work is to assess the status of interorganizational collaborations between Iranian organizations in fighting with COVID-19. We utilized social network analysis tools and methods to examine the characteristics of interorganizational collaborations network. Data were gathered through content analysis of seven of the most well-known Iranian news websites. So, the interorganizational collaborations networks between Iranian organizations and its subnetworks are identified, and their characteristics are obtained. Some of the network's features like degree distribution and homophily have been found and analyzed. The most important organizations are identified based on different centrality measures. It is necessary to mention that Basij and the National Committee on combatting corona have a fundamental role in fighting coronavirus in Iran. Also, some of the network-based evidences as the cause of Iran's success in fighting COVID-19 have been found.

A social network analysis to determine success factors of food security innovations in Tanzania

ABSTRACT To improve resistance to upcoming vulnerabilities to climate change and cope with rising food demand smallholders in Sub-Saharan Africa need to upgrade their local food systems. Upgrading strategies (UPS) within existing food value chains have the potential to secure food at the local to regional level. In this paper, we determined the roles of actors and their interlinkages and motivations in UPS, and identified possible success factors of group performance within different UPS in Tanzania: a) tied ridges, b) optimised market-oriented grain storage, c) maize sheller and d) millet thresher. We used individual interviews, via the social network analysis tool Net-Map, as well as focus group discussions. The results show that actors’ motivations for participating in a UPS group differed between groups and between actors. Important success factors derived from these findings were favorable village locations followed by sociability and personal contact among stakeholders and with researchers. These results provide the basis for future strategic planning that will enhance participation and a potential up-scaling.

Assessing Governments’ Emergency Responses to the COVID-19 Outbreak Using a Social Network Analysis (SNA)

One of the effective ways of preventing the spread of the COVID-19 outbreak is dependent on non-pharmaceutical interventions due to the absence of a vaccine against the virus. This study aims to assess organizational emergency responses to COVID-19 using a social network analysis (SNA) method via Gephi. The data were obtained from a weekly Indonesian magazine, TEMPO, which reported on the Indonesian government’s response to COVID-19 from early March to early April 2020. One hundred and fifty actors represent the nodes and 180 connections denoted as edges are explored and analyzed using the SNA tool. The study found that the emergency response to the pandemic consisted of less solid, non-traditional structural interactions, and that the head of the task force played a lesser role in the response to this outbreak. The content analysis revealed that the team members’ issues of concern included coordination, hoaxes and fake issues, instruction/consultation, social distancing, and the lack of testing equipment. The results of the study are expected to contribute to the literature on research on emergency responses to pandemics. The findings also contribute to the development of the design of organizational emergency policies in the contexts of large-scale pandemics.

COVID-19 pandemic and food poverty conversations: Social network analysis of Twitter data.

This novel and mixed-method study investigated food poverty conversations at the beginning of the COVID-19 pandemic and the subsequent national lockdown on the social media platform Twitter. NodeXL Pro software was used to collect tweets using the terms 'food' and 'poverty' in any order somewhere in a tweet sent on selected days between April 5 and May 23, 2020. The data obtained from NodeXL Pro were cleaned. Social network analysis tools were used to analyse and visualise our data. Using this method, sentiment-related words (positive or negative words), the top (the most mentioned) 10 hashtags, top words and top word pairs were identified. The patterns of word pairs communicated in our network were visualised based on each word pair's frequency. This also enabled us to carry out a content analysis to create coding of the word pairs' data. A total of 81249 tweets were identified that contained the terms 'food' and 'poverty'. Our findings revealed that individuals' tweets overwhelmingly contained views about the increase in hunger, food poverty and food insecurity due to the COVID-19 pandemic. Twitter users perceived that when the pandemic measures began, many food-secure families were pushed into food insecurity due to a rapid rise in unemployment and rising poverty due to the quarantine and stay-at-home instructions in place at the time. They also addressed the sharp rise in food poverty being driven by panic buying, food shortages, food affordability and disruptions in food supply and food systems. Our analysis of this data suggests that to mitigate food poverty or to prevent a 'hunger pandemic' for future pandemic emergencies, comprehensive and longer term policy responses and economic supports are needed to strengthen the resilience of food systems. However, the highlighted limitations of this study must be considered.

Link prediction in organizational social network based on e-mail communication

Last years one can observe a growing interest of researchers and practitioner in organizational social networks based on different forms of relations between employees. Such networks may refer to formal or informal relations between employees. The former of these relations may stem for example from the position of each person in an organization, and the latter may refer to relations between employees based for example on trust or confidence. Social network analysis tools provide several measures which allow to analyze different properties of such networks. The paper focuses on link prediction in organizational social network, where relations between employees are defined by intensity of digital communication (e-mail channel) between them. Different similarity measures have been considered by Authors. Basing on similarity scores calculated for all non-existing links in the network, new potential links with highest likelihood of their formation have been discovered. The results of the experiment (predicted links) have been compared with the results obtained by solving a community detection problem in the same network. They have been also compared with a real structure of the organization investigated in the experiment. The experiment confirmed that the results of solving a link prediction problem in the organizational network may support the process of detecting communities in the network, but it strongly depends on the similarity measure used.

Çevik Yazılım Geliştirme Metodolojisi Üzerine Bir Üçüncül Çalışma ve Sosyal Ağ Analizi

In recent years, there has been an increasing interest in research and application of agile software development. In academic literature, the number of systematic literature reviews (SLRs), systematic mapping studies, and unsystematic reviews has also increased. One of the aims of this article is to create a tertiary study of SLRs on agile software development research topics between 2013 and 2018. Our second goal is to provide an in-depth analysis with data including reference, author, institution, etc. belonging to this tertiary study, and to provide an in-depth analysis for software engineering researchers and practitioners to reveal the relationships of developments in this field. We applied a two-stage research method within the scope of the study. First, we obtained and examined the systematic literature reviews published in the field of agile software development from Web of Science, Science Direct, Scopus, and IEEE academic databases. Second, we visualized the publications examined within the scope of the tertiary study, and revealed the relationships between publications, researchers, institutions, publication sources, and countries with VOSviewer and Gephi social network analysis tools. The findings obtained as a result of the study can be summarized as the fact that developing countries have more studies on the subject, the cited publications are mostly from developed countries, contrary to what is expected, distant countries are more in cooperation, and the number of citations is not directly proportional to the number of publications.

Twitter Based Digital Social Movement Pattern to Fight COVID-19

Twitter has become a tool for people to trigger a social change, like what is happening right now during COVID-19 outbreaks. Most people are using social media platforms to express their perspectives. For the first time, this research aimed to analyze the pattern of a social movement that happened during COVID-19 Outbreaks by analyzing the Twitter dataset contains 23,476 tweets worldwide with the #COVID19 hashtag which was obtained from 02 March to 09 April 2020. Social Network Analysis tools are used to understand the pattern of movement. This research concluded that if the Government and Mainstream Media Twitter account triggered the conversation in the social media platform, followed by the activists and celebrities who engage in conversation between their followers, an ordinary person spread the point of view of the Government and Mainstream Media across their conversation network. The COVID-19 hashtag successfully engaged 10 protest clusters, which pushed the people to fight against COVID-19 in their countries, mostly targeting the government-related account. The digital social movement pattern is relatively different from the traditional social movement, even it has the same steps, which emerge, coalesce, bureaucratise, and the movement itself, but it takes place in the Digital Public Sphere without any social or political boundaries. The digital social movement forced the government to implement a better policy to fight the COVID-19 Pandemic, including to close the national border to prevent unnecessary effects of International Migration.

How Members of Covert Networks Conceal the Identities of Their Leaders

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.

Adolescent intergroup connections and their developmental benefits: Exploring contributions from social network analysis

AbstractBecause diverse school settings provide students with opportunities to form same‐ and cross‐group relationships with youth of various ethnicities and races, an important question arises: How are these opportunities taken up, and what are the developmental implications of doing so? Accumulating evidence suggests that these connections are beneficial to youth. Yet, we currently have a limited understanding of the specific peer mechanisms that produce these benefits. Addressing this gap requires adopting a relational perspective and using social network analysis (SNA) tools to characterize the theorized developmental mechanisms through which intergroup connections promote adolescent psychological and academic adjustment. To do so, we present an integrative account in which intergroup connections are viewed as developing and influencing adolescent outcomes in the context of broader peer networks. We then discuss the need to disentangle peer network selection from peer influence dynamics to provide an accurate account of multiple processes through which intergroup connections shape development and briefly explore how these goals are achieved by using statistical approaches to modeling of social networks. This review seeks to guide the next generation of research to more thoroughly test and refine the developmental theory and advance knowledge that will inform interventions to promote intergroup connections and their academic and socio‐emotional benefits.

Use of Social Network Analysis for Tax Control in Spain

The Spanish Tax Agency is an experienced user of big data and has now deployed social network analysis (SNA) tools. SNA tools have led to a qualitative leap in such wide-ranging areas as tax collection, enforcement, control of ultra-high-net-worth individuals, and money laundering. This paper presents a comprehensive overview of the different lines of research, strategies and results of nine projects over the last five years, including the lessons learned. We present the best practices in pattern discovery, the tools developed for the control of big fortunes and the strategy developed to create a bridge between expert knowledge and SNA technologies. We highlight the results of investigating interposed entities used to channel personal remuneration, complex corporate structures, and opaque companies.

Synergistic networks of COVID-19’s top papers

PurposeSynergy indicators and social network analysis (SNA), as practical tools, provide the possibility of explaining the pattern of scientific collaboration and visualization of network relations. Recognition of scientific capacities is the basis of synergy. The present study aims to measure and discover the synergistic networks of COVID-19’s top papers at the level of co-authorship, countries, journals, bibliographic couples and titles.Design/methodology/approachThe synergy indicator, co-authorship co-citation network analysis methods were applied. The research population comprises COVID-19’s top papers indexed in Essential Science Indicator and Web of Science Core Collection 2020 and 2021. Excel 2016, UCINET 6.528.0.0 2017, NetDraw, Ravar Matrix, VOSviewer version 1.6.14 and Python 3.9.5 were applied to analyze the data and visualize the networks.FindingsThe findings indicate that considering the three possible possibilities for authors, countries and journals, more redundancy and information are created and potential for further cooperation is observed. The synergy of scientific collaboration has revealed that “Wang, Y,” “USA” and “Science of the Total Environment” have the most effective capabilities and results. “Guan (2020b)” and “Zhou (2020)” are bibliographic couplings that have received the most citations. The keywords “CORONAVIRUS DISEASE 2019 (COVID-19)” were the most frequent in article titles.Originality/valueIn a circumstance that the world is suffering from a COVID-19 pandemic and all scientists are conducting various researches to discover vaccines, medicines and new treatment methods, scientometric studies, and analysis of social networks of COVID-19 publications to be able to specify the synergy rate and the scientific collaboration networks, are not only innovative and original but also of great importance and priority; SNA tools along with the synergy indicator is capable of visualizing the complicated and multifaceted pattern of scientific collaboration in COVID-19. As a result, analyses can help identify existing capacities and define a new space for using COVID-19 researchers’ capabilities.

The structure of L2 lexical-semantic networks as seen from a social network perspective

Abstract This study employed a social network analysis tool to investigate the organization of L2 lexical-semantic networks. A total of 49 Chines EFL learners of English completed a semantic fluency task in English. A lexical-semantic network was established on the data collected from the semantic fluency task. We conducted a CONCOR analysis to distinguish the central words from the peripheral ones in the lexical-semantic network. The relevance of three distributional features to the centrality of the words in the L2 lexical-semantic network was examined respectively. In addition, we analyzed the general explanatory effect of each of the three features on centrality. The results based on the distributional features are significantly correlational and report positive explanatory effects. In addition, words of similar distributional features were found to connect in a way that reflects semantic feature effects. Finally, theoretical, methodological, and pedagogical implications of the findings were discussed.

¿A quién escucha Bolsonaro en contexto de emergencia? Construyendo un marco analítico para el estudio de las redes informales de asesoría presidencial durante la pandemia de Covid-19 en Brasil

A pesar de las evidencias sobre los asesores informales de los presidentes en América Latina, la literatura sobre el tema aún es incipiente. Esteartículo investiga el escenario informativo que rodeó al presidente brasileño,Jair Bolsonaro, de enero a abril de 2020, un período de toma de decisionespresidenciales sobre las medidas para combatir la pandemia de Covid-19. Paraeso, se realiza un estudio de caso de las interacciones establecidas por el presidente brasileño basado en los registros de la Agenda del Presidente. Se utilizanherramientas de análisis de redes sociales (SNA) para examinar las evidencias.Los resultados indican que el Ministerio de Salud no fue el principal canal deinformación del presidente al principio de la pandemia a pesar de su papel central en la estructura de gobernanza nacional de emergencia en salud pública.Además, revelan la elección del presidente de utilizar la presidencia como suprincipal soporte informativo, incluso fortaleciéndolo a través de sus poderes. 

Research output, intellectual structures and contributors of digital humanities research: a longitudinal analysis 2005–2020

PurposeThis study aims to update and extend previous efforts gauging the status of the quickly evolving field of digital humanities (DH). Based on a sample of directly relevant DH literature during 2005–2020 from Web of Science, the study conducts a longitudinal examination of the research output, intellectual structures and contributors.Design/methodology/approachThe study applies bibliometric methods, social network analysis and visualization tools to conduct a longitudinal examination.FindingsThe research output and scope of DH topics has grown over time with a widening and deepening field in four major development stages. Through both term frequency and term co-occurrence relationship networks, this study further identifies four major reoccurring topics and themes of DH research: (1) collections and contents; (2) technologies, techniques, theories and methods; (3) collaboration, interdisciplinarity and support and (4) DH evolution. Finally, leading DH research contributors (authors, institutions and nations) are also identified.Originality/valueThis study utilizes a greater number of and richer subject sources than previous efforts to identify the overall intellectual structures of DH research based on key terms from titles, abstracts and author keywords. It expands on previous efforts and furthers our understanding of DH research with more recent DH literature and richer subject sources from the literature.

Knowledge sharing through social networks within construction organisations: case studies in Australia

Knowledge sharing through social networks is becoming an increasingly important mechanism for effective management of construction projects, which are regarded as heavily knowledge-intensive and relying on experiential knowledge. However, little is known on how social networks exist and their role in knowledge sharing within construction organisations in Australia. The aim of this study is to identify knowledge sharing processes within social networks in construction organisation using social network analysis (SNA). The research method for this study takes an interpretive stance using a case study approach to gain an in-depth insight. Accordingly, several mini cases (networks) within a single construction organisation were chosen for the study. Data collected through semi-structured questionnaires with key actors in each network were analysed using SNA tool. Consequently, a range of knowledge sharing processes adopted were found along with teams possessing higher network tie strength implementing structured knowledge sharing processes. Overall, the study found that networks portraying relational social capital dimension are the strongest ties. Although, the study is a case study with a single construction organisation, the findings are useful for strengthening network ties in similar construction organisations. The research also offers a method to identify strengths of networks using SNA tool for any organisation.

Tell me a story about yourself: The words of shopping experience and self-satisfaction

In this paper we investigate the verbal expression of shopping experience obtained by a sample of customers asked to freely verbalize how they felt when entering a store. Using novel tools of Text Mining and Social Network Analysis, we analyzed the interviews to understand the connection between the emotions aroused during the shopping experience, satisfaction and the way participants link these concepts to self-satisfaction and self-identity. The results show a prominent role of emotions in the discourse about the shopping experience before purchasing and an inward-looking connection to the self. Our results also suggest that modern retail environment should enhance the hedonic shopping experience in terms of fun, fantasy, moods, and emotions.

A comparative study of local broadcasters' Twitter utilization in different market conditions

Although the importance of over-the-air broadcasting has gradually dwindled in the entire media market, no one cannot deny local broadcasters' significant contribution to serving informational needs in local communities (FCC, 1999). Regarding local television's Twitter use for promotion and branding, Greer and Ferguson (2011) examined Twitter sites of 488 local television stations in the U.S. to identify the relationship between stations' status and content of stations' Twitter sites. They found that most local television stations offer news stories but barely direct viewers to their actual on-air programming. In their other study (Ferguson and Greer, 2011), they analyzed 111 local radio stations' Twitter sites to understand how regional radio stations use their business. The previous studies successfully demonstrate how local broadcasters use Twitter, but they do not capture any differences among broadcasters with different conditions. This study empirically analyzes how local broadcasters from different conditions utilize Twitter to adapt to the digitalized interactive media environment. Local broadcasters' social media performance can differ because American local broadcast markets differ in market size (Designated Market Area) and funding system (commercial vs. public), and station ownership. Thus, the scope of this study is limited to two extreme cases for comparison's sake. For comparative analysis, this study will analyze two different local different broadcasters groups – one group from Nielsen's top 20 DMA and another group from the bottom 20 DMA. Local broadcasters' Tweets from two other groups are analyzed to address the differences in Twitter uses 1) between public and commercial stations, and 2) between high-resourced (top DMA) and low-resourced (bottom DMA) market. This study will implement two methods to investigate the local broadcasters' local service activities in social network platforms, Twitter. First, website research collects qualitative and quantitative information directly from the studied websites. The website research will delineate the local broadcasters' current social media adoption. Second, network analysis of Twitter networks conducts to describe the complex conversational interaction within the Twitter network. Network analysis (Hanneman & Riddle, 2005) is one way to visualize and analyze how social media networks are connected, how they interact, and which agent is most influential in networks. The open-source program NodeXL will be used for network analysis and visualization of study results among the social network analysis tools. As a result, this study will address whether local broadcasters, which is often considered a thing of the past, still can justify their existence in the age of fiber-optic broadband and app-based mobile-centered media environment.

Barriers impeding sustainable project management: A Social Network Analysis of the Iranian construction sector

Making project management practices sustainable is an essential component to the broader mission of making the construction industry, as a whole, truly sustainable. In pursuit of this objective, numerous studies have collectively identified a plethora of barriers inhibiting construction project management sustainable transition. While such knowledge is instructive, it presents its own barrier as prevailing challenges cannot be tackled simultaneously and redressing barriers individually (and in isolation) has proven ineffective. This research therefore seeks to examine the interrelationship and dependency of these barriers, doing so in the context of the Iranian construction industry, and by using statistical means to identify underlying meta-barrier factors. Extracted from the extant literature, thirty initial barriers are defined and delineated to inform a survey data collection instrument delivered to Iranian industry experts. Of 454 questionnaires distributed to construction professionals, 176 valid responses were returned and Social Network Analysis (SNA) tools were adopted to analyse the data. Five core thematic clusters of barriers were observed to impede the industry's project management sustainability transition, namely: 1) project context; 2) knowledge; 3) investment; 4) community; and 5) strategy. Based upon these five meta clusters of barriers, future industry and government policies could set frameworks for moving towards sustainability and identify the root causes of the failure in their projects' sustainable practices. The research concludes by delineating future direction for further research investigation. This study contributes to the field through analysing the linkages between barriers and distils them into five broad problem sets. This method provides a more manageable approach and offers industry practitioners in Iran holistic insight on how to effect positive change towards sustainable project management practices, and in turn, a sustainable construction industry. In so doing, the study provides lessons for other developing countries.