scholarly journals Antisemitism and Covid-19 on Twitter. The search for hatred online between automatisms and qualitative evaluation

2021 ◽  
Vol 21 (3) ◽  
pp. 288-304
Author(s):  
Stefano Pasta ◽  
Milena Santerini ◽  
Erica Forzinetti ◽  
Marco Della Vedova
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Methodology ◽  
Hate Speech ◽  
Qualitative Evaluation ◽  
Automatic Classification ◽  
Financial Power

The article presents a case study on Antisemitic hate speech in Twitter in the period September 2019 - May 2020, with a particular focus on the months of the Covid-19 emergency. The corpus, consisting of 160.646 tweets selected by keywords, was investigated in terms of the amount of hate for each month, rhetoric and forms of Antisemitism. The analysis is carried out through social network analysis (SNA) techniques, with the goal of understanding whether it is possible to automate the process of identifying Antisemitic hatred. 26.11% of tweets contain hatred, that prejudice is the most common rhetoric (44%) and association with financial power the prevailing form (74%). The sample was also compared with another research methodology that only detects the presence of hate words. It emerges that, in addition to an in-depth knowledge of the phenomenon, it is necessary to integrate the automatic classification phase with the manual contribution.   Antisemitismo e Covid-19 in Twitter. La ricerca dell’odio online tra automatismi e valutazione qualitativa.   L’articolo presenta un caso studio sul discorso d’odio antisemita in Twitter nel periodo settembre 2019 - maggio 2020, con un particolare affondo sui mesi dell’emergenza Covid-19. Il corpus, composto da 160.646 tweet selezionati per parole chiave, è stato indagato in termini di quantità di odio per mese, retoriche utilizzate e forme di antisemitismo. L’analisi è svolta attraverso le tecniche di social network analysis (SNA), con l’obiettivo di capire se sia possibile automatizzare il processo di individuazione dell’odio antisemita. Il 26.11% dei tweet contiene odio, che il pregiudizio è la retorica più presente (44%) e l’associazione al potere finanziario la forma prevalente (74%). Il campione è stato altresì confrontato con un’altra metodologia di ricerca che rileva la sola presenza di hate words. Emerge che, oltre una conoscenza approfondita del fenomeno, occorre integrare la fase di classificazione automatica con l’apporto manuale.

scholarly journals Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery

2015 ◽  
Vol 6 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Iraj Mohammadfam ◽  
Susan Bastani ◽  
Mahbobeh Esaghi ◽  
Rostam Golmohamadi ◽  
Ali Saee
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Response ◽  
Oil And Gas ◽  
The Social ◽  
Response Team ◽  
Gas Refinery

scholarly journals Identifying Integration and Differentiation in a Hospital’s Logistical System: A social network analysis of a case study

2020 ◽  
Author(s):  
Annelies van der Ham ◽  
Frits Van Merode ◽  
Dirk Ruwaard ◽  
Arno Van Raak
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Organization Theory ◽  
Exploratory Study ◽  
Case Study Research ◽  
Study Research ◽  
And Performance

Abstract Background Integration, the coordination and alignment of tasks, has been promoted widely in order to improve the performance of hospitals. Both organization theory and social network analysis offer perspectives on integration. This exploratory study research aims to understand how a hospital’s logistical system works, and in particular to what extent there is integration and differentiation. More specifically, it first describes how a hospital organizes logistical processes; second, it identifies the agents and the interactions for organizing logistical processes, and, third, it establishes the extent to which tasks are segmented into subsystems, which is referred to as differentiation, and whether these tasks are coordinated and aligned, thus achieving integration.Methods The study is based on case study research carried out in a hospital in the Netherlands. All logistical tasks that are executed for surgery patients were studied. Using a mixed method, data were collected from the Hospital Information System (HIS), documentation, observations and interviews. These data were used to perform a social network analysis and calculate the network metrics of the hospital network.Results This paper shows that 23 tasks are executed by 635 different agents who interact through 31,499 interaction links. The social network of the hospital demonstrates both integration and differentiation. The network appears to function differently from what is assumed in literature, as the network does not reflect the formal organizational structure of the hospital, and tasks are mainly executed across functional silos. Nurses and physicians perform integrative tasks and two agents who mainly coordinate the tasks in the network, have no hierarchical position towards other agents. The HIS does not seem to fulfill the interactional needs of agents. Conclusions This exploratory study reveals the network structure of a hospital. The cross-functional collaboration, the integration found, and position of managers, coordinators, nurses and doctors suggests a possible gap between organizational perspectives on hospitals and reality. This research sets a basis for further research that should focus on the relation between network structure and performance, on how integration is achieved and in what way organization theory concepts and social network analysis could be used in conjunction with one another.

scholarly journals Examining the Diffusion of Research-Based Instructional Strategies Using Social Network Analysis: A Case Study of SCALE-UP

2020 ◽  
Author(s):  
Xaver Neumeyer ◽  
Kathleen Foote ◽  
Robert Beichner ◽  
Melissa Dancy ◽  
Charles Henderson
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Instructional Strategies ◽  
Scale Up

Case Study

2021 ◽  
pp. 345-366
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
User Research ◽  
Social Games ◽  
Work Done

This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.

scholarly journals The Mechanism of Social Organization Participation in Natural Hazards Emergency Relief: A Case Study Based on the Social Network Analysis

2019 ◽  
Vol 16 (21) ◽  
pp. 4110 ◽  
Author(s):  
Yingxin Chen ◽  
Jing Zhang ◽  
Pandu R. Tadikamalla ◽  
Lei Zhou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Management ◽  
Social Organization ◽  
Natural Hazards ◽  
Quantitative Research ◽  
Emergency Relief ◽  
The Social

The uncertainty and complexity of natural hazards put forward new requirements for emergency management systems. In order to deal with natural hazards effectively, it is important to build a cooperative network between government organizations and social organizations. The social network analysis method is adopted, the April 2013 Ya’an China earthquake is taken as a case study, the institutionalized emergency organization network before the disaster and the actual response organization network after the disaster are analyzed, and centrality, between centrality, closeness centrality and core-periphery are calculated. Through qualitative and quantitative research, the functions of social organization in the process of natural hazards emergency relief are revealed, the role orientation of social organization in the emergency management network is analyzed, and the influence factors of the social organization participation in the natural hazards relief is pointed out. Research results will help to promote the cooperation between social organization and government, and improve the efficiency of natural hazards emergency relief.

scholarly journals Social Networks and Supply Chain Management in Fish Trade

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402093181
Author(s):  
Carmen Pedroza-Gutiérrez ◽  
Juan M. Hernández
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Real Life ◽  
Primary Data ◽  
Structured Interviews ◽  
Knowledge Based ◽  
The Relationship

This study aims to construct a theoretical framework to analyze the elements of the network structure and the relationship system within the seafood supply chain. The scope of the investigation is to evaluate how these elements influence the flow of products and the efficiency of the seafood supply chain and why these social interactions can create value and enhance competitive advantage. The model combines the resource- and knowledge-based view and the social network analysis applied to seafood supply chains. To demonstrate the application of the model, two theoretical examples and a real case study of the Mercado del Mar in Guadalajara, Mexico, are used. Primary data are obtained from semi-structured interviews, social network analysis metrics, and qualitative analysis. Findings are based on the analysis of theoretical examples and must be considered with caution. Nevertheless, the observations in the examples and case study provide new arguments to the relationship between the pattern of interrelationship and the efficiency of a supply chain. This study emphasizes the necessity of combining quantitative and qualitative analyses to understand and explain real-life supply networks.

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