Cross-generational coalitions and well-being: A multivariate analysis of social network data.

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.

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

Endocrine Practice ◽

10.1016/s1530-891x(20)42480-2 ◽

2015 ◽

Vol 21 ◽

pp. 301

Author(s):

Armand Krikorian ◽

Lily Peng ◽

Zubair Ilyas ◽

Joumana Chaiban

Keyword(s):

Social Network ◽

Network Data ◽

Social Network Data

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The Multilevel p2 Model

Methodology ◽

10.1027/1614-2241.2.1.42 ◽

2006 ◽

Vol 2 (1) ◽

pp. 42-47 ◽

Cited By ~ 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.

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Topic Modeling in Large Scale Social Network Data

SSRN Electronic Journal ◽

10.2139/ssrn.2720333 ◽

2015 ◽

Cited By ~ 1

Author(s):

Aman Ahuja ◽

Wei Wei ◽

Kathleen M. Carley

Keyword(s):

Social Network ◽

Topic Modeling ◽

Large Scale ◽

Network Data ◽

Social Network Data

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

Data in Brief ◽

10.1016/j.dib.2021.106898 ◽

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

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Social Networks and Loneliness During the COVID-19 Pandemic

Socius Sociological Research for a Dynamic World ◽

10.1177/2378023120985254 ◽

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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