Who is responsible for automated driving? A macro-level insight into automated driving in the United Kingdom using the Risk Management Framework and Social Network Analysis

2019 ◽  
Vol 81 ◽  
pp. 102904 ◽  
Author(s):  
Victoria A. Banks ◽  
Neville A. Stanton ◽  
Katherine L. Plant
Keyword(s):  
Risk Management ◽  
United Kingdom ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Automated Driving ◽  
Management Framework ◽  
Risk Management Framework ◽  
The United Kingdom ◽  
Insight Into

scholarly journals COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

10.2196/19458 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e19458 ◽  
Author(s):  
Wasim Ahmed ◽  
Josep Vidal-Alaball ◽  
Joseph Downing ◽  
Francesc López Seguí
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
United Kingdom ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Conspiracy Theory ◽  
Fake News ◽  
The United Kingdom ◽  
Twitter Data

Background Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. Objective The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. Methods This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Richard Thomas Lowe (1802–1874) and his correspondence networks: botanical exchanges from Madeira

2021 ◽  
Vol 48 (2) ◽  
pp. 377-395
Author(s):  
S. Mesquita ◽  
M. Menezes De Sequeira ◽  
C. Castel-Branco
Keyword(s):  
United Kingdom ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Knowledge ◽  
Scientific Communication ◽  
Natural Sciences ◽  
The United Kingdom ◽  
Correspondence Networks

The growth of scientific knowledge in the natural sciences in the nineteenth century to a large extent depended on networking and communication between naturalists. Our case-study illustrates such forms of scientific communication using a social network analysis (SNA) approach for studying the relationships of the Reverend Richard Thomas Lowe, an English naturalist who lived in Madeira from 1826 to 1852, and continued to visit until his death in a shipwreck in 1874. During his lifetime, he established a network of contacts mainly in the United Kingdom and in Madeira, which enabled him to develop and publish his pioneering work on the local flora, including A manual flora of Madeira and the adjacent islands of Porto Santo and the Desertas.

scholarly journals COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data (Preprint)

2020 ◽  
Author(s):  
Wasim Ahmed ◽  
Josep Vidal-Alaball ◽  
Joseph Downing ◽  
Francesc López Seguí
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
United Kingdom ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Conspiracy Theory ◽  
Fake News ◽  
The United Kingdom ◽  
Twitter Data

BACKGROUND Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. OBJECTIVE The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. METHODS This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. RESULTS Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. CONCLUSIONS The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

scholarly journals Improvised Music in Berlin 2012-13: A Brief Ethnographic Portrait

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Tom Arthurs
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Structure ◽  
Point Of View ◽  
Music Making ◽  
Improvised Music ◽  
Phd Thesis ◽  
Point Of Departure ◽  
Insight Into

This paper uses approaches from ethnography and Social Network Analysis to provide a brief insight into the practical, economic and social structure of Berlin’s Improvised Music scene during 2012 and 2013. The findings presented here address imbalances of gender and race, and highlight the (often difficult) financial reality of a life in Improvised Music. Audience, venues and performers are portrayed in order to provide an entry point for those unfamiliar with Improvised Music communities, and to offer an empirically researched point of departure for those already acquainted with such musicians and practices. This paper is an adaptation of parts of my PhD thesis “The Secret Gardeners: An Ethnography of Improvised Music in Berlin (2012-13),” which addresses the aesthetics, ideologies and practicalities of contemporary European Improvised Music-making from the point of view of 34 key practitioners and “expert” listeners.

scholarly journals Using Google Maps to Show the Pattern of International Co-Author Collaboration in Pharmaceutics

2018 ◽  
pp. 1-9
Keyword(s):  
Drug Delivery ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Graphical Representations ◽  
Deep Insight ◽  
Microsoft Excel ◽  
Google Maps ◽  
Future Studies ◽  
Insight Into

Objective: To understand international co-author collaboration in pharmaceutics and to visualize results by Google maps and social network analysis (SNA). Methods: Selecting 311 abstracts from the Medline based on keyword pharmaceutics [journal], we reported following features of pharmaceutics: (1) nation distribution across continents; (2) main keywords frequently displayed in papers; (3) the eminent author in pharmaceutics. We programmed Microsoft Excel VBA for extracting data from Medline. Google Maps and SNA Pajek software show graphical representations of pharmaceutics. Results: We found that (1) the most number of papers in nations are from U.S.(81, 16.05%) and Japan(34, 10.93%); (2) the most linked keywords are Pharmacokinetics and drug delivery; (3) the eminent authors are Muhammad Sohail Arshad(UK) and Takeshi Yokoo(Japan). Conclusion: Social network analysis provides wide and deep insight into relationships of entities we interested. The results drawn from Google maps can provide more information to future studies in academics.

Dynamic Social Network Mining

2013 ◽  
pp. 122-144
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Discovery ◽  
Network Mining ◽  
Social Network Mining ◽  
Dynamic Social Network ◽  
Main Work ◽  
Insight Into

This Chapter overviews most recent data mining approaches proposed in the context of social network analysis. In particular, it aims at classifying the proposed approaches based on both the adopted mining strategies and their suitability for supporting knowledge discovery in a dynamic context. To provide a thorough insight into the proposed approaches, main work issues and prospects in dynamic social network analysis are also outlined.

scholarly journals Topology of the World Tourism Web

2021 ◽  
Vol 11 (5) ◽  
pp. 2253
Author(s):  
Katarina Kostelić ◽  
Marko Turk
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Biological Network ◽  
The World ◽  
Source Countries ◽  
And Diffusion ◽  
Insight Into ◽  
Diffusion Properties

The applications of social network analysis to the world tourism network are scarce, and a research update is long overdue. The goal of this research is to examine the topology of the world tourism network and to discuss the meaning of its characteristics in light of the current situation. The data used for the analysis comprise 193 target countries, 242 source countries, and 17,022 links, which is an overall 1,448,285,894 travels in 2018. Social network analysis is applied to the data to determine network topological and diffusion properties, as well as the network structure and its regularities (does it behave more as a social or a technological/biological network?). While results presented in this paper give a thorough insight into the world tourism network in the year 2018, they are only a glimpse in comparison to the possibilities for further research.

Social Network Analysis for the Implementation of Sendai Framework for Disaster Risk Reduction in Iran

2019 ◽  
Author(s):  
Homa Yousefi Khoshsabegheh ◽  
Ali Ardalan ◽  
Amir Hossein Takian ◽  
Leila Hedayatifar ◽  
Abbas Ostadtaghizadeh ◽  
...  
Keyword(s):  
Risk Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Optimization Methods ◽  
Optimization Method ◽  
Disaster Risk ◽  
Civil Defense ◽  
Disaster Risk Management ◽  
The Status

Abstract Background: Over recent years, the exposure of people and assets to disasters has been faster than reducing vulnerability in all countries. As a result, new risks have been formed and losses due to disaster are progressively increasing. Suffering from significant losses in the aftermath of disasters every year, Iran is no exception. Governmental and non-governmental stakeholders are jointly responsible for managing the risks of disasters. Hence, appropriate, collaborative and timely interactions of involved organizations will play an important role in their operation, especially during disasters. Methods: In this study, we used the Social Network Analysis (SNA) to analyze the network of stakeholders in disaster risk management in Iran. Our review of literature, laws, and regulations of disaster risk management plus brainstorming identified a list of 85 stakeholders. We used the Delphi method among purposefully selected experts to score the relationship between the stakeholders. We then used the modularity optimization method to identify groups with greater interaction. Organizations with key-roles in the network and the ones in need of stronger relationships were identified through centrality measurements. Results: The density of this network was 0.75, which represented that not all the stakeholders were connected. Among all organizations identified, the National Disaster Management Organization and Civil Defense Organization showed higher influences considering their responsibilities. Conclusion: To provide a visual and tangible picture of the status and interrelationships among the stakeholders, this method identified groups with better interaction using community/cluster detection and modularity optimization methods. Understanding the current structure of the network and strengths and weaknesses of the interactions among stakeholders may help improve disaster risk management in Iran. Results of this research determine the role and importance of different organizations, their weakness, and strong points. Also, results help them to plan to strengthen their roles and solve their problems.

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