scholarly journals Student Participation in Online Content-Related Discussion and Its Relation to Students’ Background Knowledge

2020 ◽  
Vol 10 (4) ◽  
pp. 106 ◽  
Author(s):  
Miikka Turkkila ◽  
Henri Lommi
Keyword(s):  
Network Analysis ◽  
Student Participation ◽  
Background Knowledge ◽  
Online Discussions ◽  
Centrality Measures ◽  
The Social ◽  
Network Methods ◽  
Katz Centrality ◽  
Characteristic Features ◽  
University Course

This paper presents two novel network methods developed for education research. These methods were used to investigate online discussions and the structure of students’ background knowledge in a blended university course for pre-service teachers (n = 11). Consequently, these measures were used for correlation analysis. The social network analysis of the online discussions was based on network roles defined using triadic motifs instead of more commonly used centrality measures. The network analysis of the background knowledge is based on the Katz centrality measure and Jaccard similarity. The results reveal that both measures have characteristic features that are typical for each student. These features, however, are not correlated when student participation is controlled for. The results show that the structure and extension of a student’s background knowledge does not explain their activity and role in online discussions. The limitations and implications of the developed methods and results are discussed.

scholarly journals Twitter Networks’ Centrality Measures of the Association of Health Journalist (ANIS): the Case of Vaccines - Introduction and Theoretical Framework

2020 ◽  
Vol 3 (2) ◽  
pp. 12
Author(s):  
Miguel Martín Cárdaba ◽  
Rafael Carrasco Polaino ◽  
Ubaldo Cuesta Cambra
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theoretical Framework ◽  
Communication Strategy ◽  
Centrality Measures ◽  
The Social ◽  
One Year

The popularization of Internet and the rise of social networks have offered an unbeatable opportunity for anti-vaccines, especially active communicators, to spread their message more effectively causing a significant impact on public opinion. A great amount of research has been carried out to understand the behavior that anti-vaccine communities show on social networks. However, it seems equally relevant to study the behavior that communities and communicators “pro vaccines” perform in these networks. Therefore, the objective of this research has been to study how members of the Spanish Association of Health Journalist (ANIS) communicate and use the social network Twitter. More specifically, the different interactions made by ANIS partners were analyzed through the so-called “centrality measures of social network analysis” (SNA), to check the configuration of the user network and detect those most relevant by their indexes of centrality, intermediation or mentions received. The research monitored 142 twitter accounts for one year analyzing 254 twits and their 2.671 interactions. The research concluded that the network of ANIS partners on Twitter regarding vaccines has little cohesion and has several components not connected to each other, in addition to the fact that there are users outside the association that show greater relevance than the partners themselves. We also concluded that there are an important lack of planning and direction in the communication strategy of ANIS on Twitter, which limits the dissemination of important content.

scholarly journals SOCIAL NETWORK ANALYSIS FOR CHURN PREDICTION IN TELECOM DATA

2016 ◽  
pp. 295-302
Author(s):  
PUSHPA PUSHPA ◽  
Dr. Shobha G
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social World ◽  
Centrality Measures ◽  
Churn Prediction ◽  
Huge Amount ◽  
Analysis Techniques ◽  
Manual Analysis ◽  
The Social

Social Network Analysis (SNA) is a set of research procedures for identifying group of people who share common structures in systems based on the relations among actors. Grounded in graph and system theories, this approach has proven to be powerful measures for studying networks in various industries like Telecommunication, banking, physics and social world, including on the web. Since Telecommunication industries deals with huge amount of data, manual analysis of data is very difficult. In this paper we explore the Social Network Analysis techniques for Churn Prediction in Telecom data. Typical work on social network analysis includes the construction of multi-relational telecom social network and centrality measures for prediction of churners in telecom social network.

scholarly journals Farmers’ Network Analysis on Diffusion and Adoption of CAU-R1 Variety in Imphal East District of Manipur

2019 ◽  
pp. 1-17
Author(s):  
S. S. P. Jyothi ◽  
Loukham Devarani
Keyword(s):  
Network Analysis ◽  
Social Participation ◽  
Sampling Procedure ◽  
Centrality Measures ◽  
Extension Agents ◽  
Snowball Sampling ◽  
Rate Of Spread ◽  
The Social ◽  
Medium Level

Food security with increased and sustained production of the major cereal crops in India is the need of the hour. The role of farmers as informal extension agents has been depicted in many recent studies emphasising the need for studies on network linkages between the farmer communities and the stakeholders in dissemination and adoption of improved technologies. The present study has been conducted to understand the role of social networks in the diffusion of CAU-R1 variety among the farmers of Manipur. The research design employed was exploratory and the sampling procedure was mixed sampling with purposive sampling for the selection of the state, district and key farmers. Snowball sampling was used to identify other farmers in the network. The sample size was 64 farmers from eight villages in Imphal East district. The socio-economic profile of the farmers showed that majority belonged to medium age between 36 years to 50 years, medium level of innovativeness, social participation, cosmopoliteness and risk bearing ability. The Social Network Analysis measures employed for the study were the centrality measures that include the degree, closeness and betweenness centrality to identify the most central, influential and powerful actors in the network. The average in-degree and out-degree was found to be equal for all the villages with a maximum degree centrality of 16. The betweenness centralization index of the networks was very low (24.55%) indicating very slow rate of spread of information and information sharing restricted only between few actors in the network. Social participation and trainings were positively correlated while the farming experience and time taken for adoption were negatively correlated with the network measures. The outcomes revealed that there is need for a more concerted effort by the farmers and stakeholders to sensitize farmers about the variety through exposure visits, trainings, incentives and timely input supply.

Unravelling the phenotypic tangle of autistic, schizotypal and social anxiety traits: A Network Analysis

2020 ◽  
Author(s):  
Orestis Zavlis ◽  
Myles Jones
Keyword(s):  
Network Analysis ◽  
Social Anxiety ◽  
Factor Structure ◽  
Adult Population ◽  
Autism Spectrum ◽  
Underlying Structure ◽  
Schizotypal Personality ◽  
The Social ◽  
Trait Level ◽  
General Adult Population

Substantial overlap exists between schizophrenia and autism spectrum disorders, with part of that overlap hypothesised to be due to comorbid social anxiety. The current paper investigates the interactions and factor structure of these disorders at a personality trait level, through the lens of a network model. The items of the Autism Quotient (AQ), Schizotypal Personality Questionnaire Brief-Revised (SPQ-BR), and the Liebowitz Social Anxiety Scale (L-SAS) were combined and completed by 345 members of the general adult population. An Exploratory Graph Analysis (EGA) on the AQ-SPQ-BR combined inventory revealed two communities (factors), which reflected the general autism and schizotypal phenotypes. An additional EGA on all inventories validated the AQ-SPQ-BR factor structure and revealed another community, Social Anxiety (L-SAS). A Network Analysis (NA) on all inventories revealed several moderately central subscales, which collectively reflected the social-interpersonal impairments of the three disorders. The current results suggest that a combination of recent network- and traditional factor-analytic techniques may present a fruitful approach to understanding the underlying structure as well as relation of different psychopathologies.

Centrality Measures

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
State Of The Art ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multilayer Networks ◽  
Comprehensive Description ◽  
Multilayer Network ◽  
Suitable Definition ◽  
Katz Centrality ◽  
Pagerank Centrality

Defining the centrality of nodes and layers in multilayer networks is of fundamental importance for a variety of applications from sociology to biology and finance. This chapter presents the state-of-the-art centrality measures able to characterize the centrality of nodes, the influences of layers or the centrality of replica nodes in multilayer and multiplex networks. These centrality measures include modifications of the eigenvector centrality, Katz centrality, PageRank centrality and Communicability to the multilayer network scenario. The chapter provides a comprehensive description of the research of the field and discusses the main advantages and limitations of the different definitions, allowing the readers that wish to apply these techniques to choose the most suitable definition for his or her case study.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

An Introduction to Statistical Models for Networks

2020 ◽  
pp. 218-233
Author(s):  
Valentina Kuskova ◽  
Stanley Wasserman
Keyword(s):  
Social System ◽  
Exponential Family ◽  
Behavioral Science ◽  
Social Science Research ◽  
Science Research ◽  
Uniform Distributions ◽  
The Social ◽  
Special Cases ◽  
Social And Behavioral Science ◽  
Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker
Keyword(s):  
Network Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Centrality Measures ◽  
Time Interval ◽  
Direct Effects ◽  
Network Approach ◽  
Var Models ◽  
Auto Regressive ◽  
Conceptual Shift

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

scholarly journals Social constructivism in mathematics? The promise and shortcomings of Julian Cole’s institutional account

Synthese ◽  
2021 ◽  
Author(s):  
Jenni Rytilä
Keyword(s):  
Social Constructivism ◽  
Mathematical Practice ◽  
Social Constructs ◽  
Socially Constructed ◽  
The Social ◽  
Characteristic Features ◽  
Core Idea ◽  
Social Entities ◽  
In Virtue Of ◽  
Mathematical Reality

AbstractThe core idea of social constructivism in mathematics is that mathematical entities are social constructs that exist in virtue of social practices, similar to more familiar social entities like institutions and money. Julian C. Cole has presented an institutional version of social constructivism about mathematics based on John Searle’s theory of the construction of the social reality. In this paper, I consider what merits social constructivism has and examine how well Cole’s institutional account meets the challenge of accounting for the characteristic features of mathematics, especially objectivity and applicability. I propose that in general social constructivism shows promise as an ontology of mathematics, because the view can agree with mathematical practice and it offers a way of understanding how mathematical entities can be real without conflicting with a scientific picture of reality. However, I argue that Cole’s specific theory does not provide an adequate social constructivist account of mathematics. His institutional account fails to sufficiently explain the objectivity and applicability of mathematics, because the explanations are weakened and limited by the three-level theoretical model underlying Cole’s account of the construction of mathematical reality and by the use of the Searlean institutional framework. The shortcomings of Cole’s theory give reason to suspect that the Searlean framework is not an optimal way to defend the view that mathematical reality is socially constructed.

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