Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

2021 ◽  
pp. 193758672110137
Author(s):  
Jacqueline M. Burgette ◽  
Jacquelin Rankine ◽  
Alison J. Culyba ◽  
Kar-Hai Chu ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Best Practices ◽  
Health Outcomes ◽  
Organizational Behavior ◽  
Network Data ◽  
Egocentric Networks ◽  
Covariate Selection ◽  
Social Network Modeling ◽  
Egocentric Network ◽  
The Impact

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

scholarly journals Collecting egocentric network data with visual tools: A comparative study

2020 ◽  
Vol 8 (2) ◽  
pp. 223-250
Author(s):  
Betina Hollstein ◽  
Tom Töpfer ◽  
Jürgen Pfeffer
Keyword(s):  
Network Data ◽  
Thinking Aloud ◽  
Egocentric Networks ◽  
Visual Tool ◽  
Data Collection Process ◽  
Egocentric Network ◽  
Data Validity ◽  
Concentric Circles ◽  
The Impact

AbstractWhen collecting egocentric network data, visual representations of networks can function as a cognitive aid for depicting relationships, helping to maintain an overview of the relationships, and keeping the attention of the interviewees. Additionally, network maps can serve as a narration generator in qualitative and in mixed-methods studies. While varying visual instruments are used for collecting egocentric network data, little is known about differences among visual tools concerning the influence on the resulting network data, the usability for interviewees, and data validity. The article provides an overview of existing visually oriented tools that are used to collect egocentric networks and discusses their functions, advantages, and limitations. Then, we present results of an experimental study where we compare four different visual tools with regard to networks elicited, manageability, and the impact of follow-up questions. In order to assess the manageability of the four tools, we used the thinking aloud method. The results provide evidence that the decision in favor of a specific visual tool (structured vs. unstructured) can affect the size and composition of the elicited networks. Follow-up questions greatly affect the elicited networks and follow-up cues can level out differences among tools. Respondents tend to prefer the concentric circles tool, with some differences in preferences and manageability of tools between participants with low and those with high socioeconomic status. Finally, assets and drawbacks of the four instruments are discussed with regard to data quality and crucial aspects of the data collection process when using visual tools.

scholarly journals Did people really drink bleach to prevent COVID-19? A tale of problematic respondents and a guide for measuring rare events in survey data

2020 ◽  
Author(s):  
Leib Litman ◽  
Zohn Rosen ◽  
Cheskie Rosenzweig ◽  
Sarah L. Weinberger-Litman ◽  
Aaron J. Moss ◽  
...  
Keyword(s):  
Public Health ◽  
Best Practices ◽  
Health Outcomes ◽  
Survey Research ◽  
Iron Content ◽  
Heart Attack ◽  
Public Health Research ◽  
Similar Rate ◽  
The Impact ◽  
The Relationship

AbstractSociety is becoming increasingly dependent on survey research. However, surveys can be impacted by participants who are non-attentive, respond randomly to survey questions, and misrepresent who they are and their true attitudes. The impact that such respondents can have on public health research has rarely been systematically examined. In this study we examine whether Americans began to engage in dangerous cleaning practices to avoid Covid-19 infection. Prior findings reported by the CDC have suggested that people began to engage in highly dangerous cleaning practices during the Covid-19 pandemic, including ingesting household cleansers such as bleach. In a series of studies totaling close to 1400 respondents, we show that 80-90% of reports of household cleanser ingestion are made by problematic respondents. These respondents report impossible claims such as ‘recently having had a fatal heart attack’ and ‘eating concrete for its iron content’ at a similar rate to ingesting household cleaners. Additionally, respondents’ frequent misreading or misinterpreting the intent of questions accounted for the rest of such claims. Once inattentive, mischievous, and careless respondents are taken out of the analytic sample we find no evidence that people ingest cleansers to prevent Covid-19 infection. The relationship between dangerous cleaning practices and health outcomes also becomes non-significant once problematic respondents are taken out of the analytic sample. These results show that reported ingestion of household cleaners and other similar dangerous practices are an artifact of problematic respondent bias. The implications of these findings for public health and medical survey research, as well as best practices for avoiding problematic respondents in surveys are discussed.

The role of egocentric networks in achieving resilience: a case study from the apparel sector

2018 ◽  
Vol 48 (7) ◽  
pp. 682-697 ◽  
Author(s):  
Jyotirmoyee Bhattacharjya
Keyword(s):  
Supply Network ◽  
Financial Health ◽  
Multiple Perspectives ◽  
Egocentric Networks ◽  
Content Type ◽  
Study Approach ◽  
Egocentric Network ◽  
The Impact

Purpose The purpose of this paper is to explore the egocentric network-based strategies used by upstream firms to ensure their own resilience when the disruptions originate with downstream partners. Design/methodology/approach The paper adopts a case study approach as this is well-suited to the investigation of a complex phenomenon from multiple perspectives. Findings The study finds that the egocentric networks of upstream firms participating in the supply network of a retailer could ensure their own resilience even after the sudden demise of the downstream entity. Originality/value The study addresses the lack of adequate empirical research examining resilience from the perspectives of multiple entities in a supply network. It is also one of the few papers to address resilience from the perspective of upstream players in the context of a disruption originating with downstream partners. The findings suggest that the lack of visibility in relation to the financial health of more powerful downstream partners could be problematic from a supplier’s perspective. It identifies well-developed egocentric networks as being essential for minimizing consequences of limited downstream visibility and the impact on social capital.

Faculty use of Twitter in higher education teaching

2017 ◽  
Vol 9 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Kevin Hull ◽  
Julie E. Dodd
Keyword(s):  
Higher Education ◽  
Social Network ◽  
Best Practices ◽  
Student Learning ◽  
Undergraduate Education ◽  
Good Practice ◽  
Content Type ◽  
Seven Principles ◽  
The Social ◽  
The Impact

Purpose The purpose of this paper is to determine how higher education teachers are using Twitter in their classroom to engage, educate, and inform students. The results were measured against Chickering and Gamson (1987) “Seven principles for good practice in undergraduate education.” Design/methodology/approach A survey was sent to college and university educators throughout the country who were identified as teachers who use Twitter in their classroom. These educators were asked about their Twitter use, their opinions of Twitter, the impact the social network has had on student learning, the students’ reactions to using Twitter, and how Twitter supported pedagogical best practices, including the “Seven principles for good practice in undergraduate education”. Findings The educators reported that student response to using Twitter in the classroom was overwhelmingly positive and that Twitter has positively impacted student learning. The results also indicate that college educators consider that Twitter use in classes does support the seven principles. Research limitations/implications While college instructors from a wide range of institutions, locations, subject types, and experience levels were surveyed, a limitation is that only their opinions are being examined. Future research may wish to examine the Twitter accounts of these professors to determine if they are using Twitter in the manner that they think they are. Results from the survey could then be compared with the tweet content. Originality/value While previous research has examined how students use and appreciate Twitter in the classroom, this is one of the first studies to examine how the social network is implemented from an instructor viewpoint. The results demonstrate value to instructors. For instructors, the value lies in the knowledge that Twitter has had a positive impact on classroom success for students and that using the social network promotes best practices in pedagogy, supporting constructivism, experiential learning, and the “Seven principles for good practice in undergraduate education”. For administrators, the value lies in the fact that many instructors have had success using Twitter and that more should be encouraged to do the same in their classrooms.

scholarly journals Corporate Social Networks Applied in the Classroom

2016 ◽  
Vol 3 (2) ◽  
pp. 156
Author(s):  
Hugo De Juan-Jordán ◽  
María Guijarro-García ◽  
Javier Guardiola-Contreras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Best Practices ◽  
Learning Process ◽  
Online Social Networks ◽  
Business School ◽  
Classroom Dynamics ◽  
Corporate Social ◽  
The Impact

<p class="Textoindependiente21"><span lang="EN-US">The impact of online social networks has been extensive because of the new way they enable not only in terms of the relation, communication and collaboration among people, but also between people and businesses. So much so, their use is already habitual within organizations, known as Corporate Social Networks, in order to achieve the same benefits.</span></p><p class="Textoindependiente21">The present study aims to analyze the advantages these corporate social networks have in the classroom seen as a micro-organization where a group of students interact, work and collaborate during a master´s or postgraduate course. To support this research, during 2015 a corporate social network (Yammer) has been introduced to 5 groups of students of various master´s in the prestigious business school ESIC. The feedback obtained from those students and some examples of classroom dynamics prove the usefulness and great value of a corporate social network in postgraduate classes, although some common difficulties and considerations raised by the students themselves have to be taken into account in order to manage its optimal adoption in class.</p><p class="Textoindependiente21">This study also tries to propose some guidelines and best practices obtained as a result of the experience of use and the adoption of social networks in class in order to improve the learning process and innovate in the methodology applied to education.</p>

scholarly journals Impact of fixed choice design on blockmodeling outcomes

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Anja Žnidaršič
Keyword(s):  
Social Network ◽  
Research Design ◽  
Free Choice ◽  
Network Data ◽  
Social Network Data ◽  
Different Types ◽  
Two Indices ◽  
The Impact

The main goal of blockmodeling is to reveal the essential structure of the network based on patterns of relationships. Social network data usually contain different types of errors and one of them is caused by some limitation of number of selected actors in the research design. The impact of fixed choice design compared to free choice design on the results of blockmodeling are studied through simulations. The resulting blockmodels are compared with two indices where position membership of actors and the structure of the blockmodels are examined. Limiting the number of actors that can be selected has an impact on delineated blockmodel structure where the deletion of ties has higher effect than addition of them.

scholarly journals Modelling, Simulation and Social Network Data: What’s New for Public Health and Epidemiology Informatics?

2015 ◽  
Vol 24 (01) ◽  
pp. 216-219
Author(s):  
L. Toubiana ◽  
N Griffon ◽  
Keyword(s):  
Public Health ◽  
Social Network ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Network Data ◽  
Health It ◽  
Social Network Data ◽  
The Impact ◽  
Modelling Simulation

Summary Objectives: Summarize excellent current research in the field of Public Health and Epidemiology Informatics. Method: Synopsis of the articles selected for the IMIA Yearbook 2015. Results: Four papers from international peer-reviewed journals have been selected as best papers for the section on Public Health and Epidemiology Informatics. Conclusions: The selected articles illustrate current research regarding the impact and assessment of health IT and the latest developments in health information exchange.

scholarly journals Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data †

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 306
Author(s):  
Nikolaus Nova Parulian ◽  
Tiffany Lu ◽  
Shubhanshu Mishra ◽  
Mihai Avram ◽  
Jana Diesner
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Social Systems ◽  
Target Function ◽  
Centrality Measures ◽  
Network Data ◽  
Social Network Data ◽  
Empirical Networks ◽  
The Impact

Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the domain of social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of a target node according to centrality measures. This can represent the attempt of an adversary to boost or demote the degree to which others perceive individual nodes as influential or powerful. We study the impact of adversarial attacks on targets and victims, and identify metric-based security strategies to mitigate such attacks. We conduct a series of controlled experiments on synthetic network data to identify attacks that allow the adversary to achieve their objective with a single move. We then replicate the experiments with empirical network data. We run our experiments on common network topologies and use common centrality measures. We identify a small set of moves that result in the adversary achieving their objective. This set is smaller for decreasing centrality measures than for increasing them. For both synthetic and empirical networks, we observe that larger networks are less prone to adversarial attacks than smaller ones. Adversarial moves have a higher impact on cellular and small-world networks, while random and scale-free networks are harder to perturb. Also, empirical networks are harder to attack than synthetic networks. Using correlation analysis on our experimental results, we identify how combining measures with low correlation can aid in reducing the effectiveness of adversarial moves. Our results also advance the knowledge about the robustness of centrality measures to network perturbations. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and developing solutions to improving network security.

scholarly journals Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

2021 ◽  
Vol 11 (11) ◽  
pp. 4768
Author(s):  
Sanaa Kaddoura ◽  
Maher Itani ◽  
Chris Roast
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Network Data ◽  
Arabic Text ◽  
Social Network Data ◽  
Dialectal Arabic ◽  
The Impact ◽  
Modern Standard

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

What Is Special About Social Network Analysis?

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 2-6 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Jeroen K. Vermunt
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Models ◽  
Network Modeling ◽  
Network Data ◽  
Short Introduction ◽  
Social Network Data ◽  
Social Network Modeling ◽  
Recent Developments

In a short introduction on social network analysis, the main characteristics of social network data as well as the main goals of social network analysis are described. An overview of statistical models for social network data is given, pointing at differences and similarities between the various model classes and introducing the most recent developments in social network modeling.

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