Sensitivity of social network analysis metrics to observation noise

2004 ◽  
Author(s):  
B.E. Thomason ◽  
T.R. Coffman ◽  
S.E. Marcus
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Observation Noise ◽  
Network Analysis Metrics

scholarly journals Using Social Network Analysis and Social Capital to Identify User Roles on Polarized Political Conversations on Twitter

2019 ◽  
Vol 5 (2) ◽  
pp. 205630511984874 ◽  
Author(s):  
Raquel Recuero ◽  
Gabriela Zago ◽  
Felipe Soares
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Opinion Leaders ◽  
Information Flows ◽  
Network Metrics ◽  
Network Analysis Metrics

In this article, we discuss the roles users play in political conversations on Twitter. Our case study is based on data collected in three dates during the former Brazilian president Lula’s corruption trial. We used a combination of social network analysis metrics and social capital to identify the users’ roles during polarized discussions that took place in each of the dates analyzed. Our research identified four roles, each associated with different aspects of social capital and social network metrics: activists, news clippers, opinion leaders, and information influencers. These roles are particularly useful to understand how users’ actions on political conversations may influence the structure of information flows.

Social Media Metrics in an Academic Setup

2017 ◽  
pp. 116-130
Author(s):  
Tasleem Arif ◽  
Rashid Ali
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Present Status ◽  
The Internet ◽  
Collaboration Networks ◽  
Different Types ◽  
Network Metrics ◽  
Network Analysis Metrics

Social media is perhaps responsible for largest share of traffic on the Internet. It is one of the largest online activities with people from all over the globe making its use for some sort of activity. The behaviour of these networks, important actors and groups and the way individual actors influence an idea or activity on these networks, etc. can be measured using social network analysis metrics. These metrics can be as simple as number of likes on Facebook or number of views on YouTube or as complex as clustering co-efficient which determines future collaborations on the basis of present status of the network. This chapter explores and discusses various social network metrics which can be used to analyse and explain important questions related to different types of networks. It also tries to explain the basic mathematics behind the working of these metrics. The use of these metrics for analysis of collaboration networks in an academic setup has been explored and results presented. A new metric called “Average Degree of Collaboration” has been defined to quantify collaborations within institutions.

Social Network Analysis Metrics and Their Application in Microbiological Network Studies

2014 ◽  
pp. 251-260 ◽  
Author(s):  
Juliana Saragiotto Silva ◽  
Nancy de Castro Stoppe ◽  
Tatiana Teixeira Torres ◽  
Laura Maria Mariscal Ottoboni ◽  
Antonio Mauro Saraiva
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Studies ◽  
Network Analysis Metrics

Social network analysis of mythology field

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sümeyye Akça ◽  
Müge Akbulut
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Decision Makers ◽  
Content Type ◽  
Centrality Metrics ◽  
Subject Categories ◽  
The Subject ◽  
Number Of Publications ◽  
Network Analysis Metrics

PurposeThe main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.Design/methodology/approachIn this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.FindingsThe findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.Originality/valueThe findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.

scholarly journals POLITICAL FANDOMS AND SUPERPARTICIPANTS IN POLITICAL CONVERSATIONS ON TWITTERTwitter, social network analysis, political polarization, fandoms

2020 ◽  
Author(s):  
Gabriela Zago ◽  
Raquel Recuero ◽  
Felipe Soares
Keyword(s):  
Popular Culture ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Political Polarization ◽  
Political Position ◽  
Highly Active ◽  
Political Networks ◽  
Network Analysis Metrics

In this proposal, we discuss the role of superparticipants in political conversations on Twitter. Our hypothesis is that these highly active users show a clear political position and intentionally act to give visibility to some topics and to reduce the visibility of others, practices that are similar to those observed among fans in popular culture. In terms of methods, we use social network analysis metrics to identify the modularity of the network and users that receive more attention than others (higher indegree) or mention more other users (higher outdegree). We collected tweets related to the impeachment of the Brazilian ex-president Dilma Rousseff in 2016 in three critical dates of the process. By observing the users with higher outdegree in each network, we noticed some patterns and behaviors that can characterize those users as political fans. Our main finding is that the superparticipants with higher outdegree helped to shape the polarized networks by retweeting like-minded accounts, and thus are important and influence the study of polarized political networks on Twitter.

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