scholarly journals Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach

2021 ◽  
Vol 18 (15) ◽  
pp. 8110
Author(s):  
Federica Cugnata ◽  
Silvia Salini ◽  
Elena Siletti
Keyword(s):  
Social Network ◽  
Bayesian Networks ◽  
Composite Index ◽  
Well Being ◽  
Network Data ◽  
Official Statistics ◽  
Social Network Data ◽  
Use Of Data ◽  
Time Period ◽  
The Relationship

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT’s survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.

scholarly journals Designing Educational Analytics Tools Using Visualization Based on Behavioral, Motivational, and Social Network Data

2019 ◽  
pp. 81-93
Author(s):  
Iliya L. Musabirov ◽  
Keyword(s):  
Social Network ◽  
Social Values ◽  
Network Data ◽  
Network Approach ◽  
Social Network Data ◽  
Use Of Data ◽  
University Courses ◽  
The Social ◽  
Social Network Approach ◽  
Visualization Tools

The article presents a description of the approach to the use of data visualization in various educational Analytics tools when building University courses. In addition to the analysis of educational behavior, socio-psychological approaches, including the theory of expectations and social values, and the social network approach, are separately considered as prospects for analysis. An example of designing training Analytics using modern data analysis and visualization tools is analyzed.

Using Social Network Data to Identify Key Influencers for Social CRM Activities

2013 ◽  
pp. 256-272
Author(s):  
Goetz Greve
Keyword(s):  
Social Network ◽  
Performance Measures ◽  
Implementation Process ◽  
Social Effects ◽  
Network Data ◽  
Network Marketing ◽  
Social Network Data ◽  
Social Crm ◽  
The Social ◽  
The Relationship

Social network data can be used to identify key influencers within a company’s customer database. Key influencers are consumers that are equipped with a large and strong network of connected neighbors. Within such a strong network, marketing messages can be passed on easily via the key influencers. The purpose of the chapter is to elaborate on the social effects of customer networks and the possibility to use data from these networks for Social CRM. First, the foundations of social contagion in networks and the relationship between social effects and Social CRM performance measures are explained. Second, possible ways of data acquisition and data integration are discussed and an overview of analytical software solutions is given. Fourth, the implementation process and its challenges are elaborated. The chapter closes with an outline of further research directions.

scholarly journals Constructing “Experts” Among Peers: Educational Infrastructure, Test Data, and Teachers’ Interactions About Teaching

2018 ◽  
Vol 40 (4) ◽  
pp. 586-612 ◽  
Author(s):  
James P. Spillane ◽  
Matthew Shirrell ◽  
Samrachana Adhikari
Keyword(s):  
Social Network ◽  
School District ◽  
Test Scores ◽  
Teacher Performance ◽  
School Staff ◽  
Network Data ◽  
Social Network Data ◽  
Student Test ◽  
Best Teachers ◽  
The Relationship

Teachers’ on-the-job interactions with colleagues impact their effectiveness, yet little research has explored whether and how teacher performance predicts these interactions. Drawing on 5 years of social network data from one school district, we explore the relationship between teacher performance and teachers’ instructional advice and information interactions. Results demonstrate that higher performing teachers are not more likely to be sought out for advice; instead, higher performing teachers are more likely to seek advice. Although school staff report they can identify the “best” teachers, they generally do not rely on student test scores, instead relying on more readily accessible indicators of performance. These findings have important implications for policy and practices that seek to promote desired interactions among teachers.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Topic Modeling in Large Scale Social Network Data

2015 ◽  
Author(s):  
Aman Ahuja ◽  
Wei Wei ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Topic Modeling ◽  
Large Scale ◽  
Network Data ◽  
Social Network Data

scholarly journals Social network data of Swiss farmers related to agricultural climate change mitigation

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106898
Author(s):  
Cordelia Sophie Kreft ◽  
Mario Angst ◽  
Robert Huber ◽  
Robert Finger
Keyword(s):  
Climate Change ◽  
Social Network ◽  
Climate Change Mitigation ◽  
Network Data ◽  
Social Network Data ◽  
Change Mitigation

scholarly journals Social Networks and Loneliness During the COVID-19 Pandemic

2021 ◽  
Vol 7 ◽  
pp. 237802312098525
Author(s):  
Balazs Kovacs ◽  
Nicholas Caplan ◽  
Samuel Grob ◽  
Marissa King
Keyword(s):  
Social Network ◽  
Social Isolation ◽  
Close Relationships ◽  
Network Size ◽  
Global Network ◽  
Network Data ◽  
Network Characteristics ◽  
Face To Face ◽  
Social Network Data ◽  
Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

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