Applying Social Network Analysis to Analyze a Web-Based Community

Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

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Using Social Network Analysis to Explore Digital Student Interactions and Business Competency Learning in a Web-based Educational Platform

International Journal of Information Systems and Social Change ◽

10.4018/ijissc.2015010101 ◽

2015 ◽

Vol 6 (1) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Andrew Feldstein ◽

Kim Gower

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Web 2.0 ◽

Learning Community ◽

Student Behaviors ◽

Global Business ◽

Student Interactions ◽

Web Based ◽

Rich Data

Web 2.0 tools occupy a large part of our lives, and their use in the classroom offers instructors a unique opportunity to gather substantial information about individual and interactive student behaviors. The authors' challenge is understanding the implications of this rich data source for assessing course efficacy and student learning, and applying these insights to further enhance the development of global business competencies. This paper reviews 311 student interactions as reflected in comments exchanged in a digital social learning community and, using social network analysis, discusses the potential to use these interactions to assess student critical thinking, communication, and collaborative feedback skills. The authors conclude with implications and recommendations for instructors who want to use Web 2.0 platforms and data to enhance their understanding of student and class digital interactions, and apply this information to course enhancement.

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Researching Community in Distributed Environments

Handbook of Research on Methods and Techniques for Studying Virtual Communities ◽

10.4018/978-1-60960-040-2.ch030 ◽

2011 ◽

pp. 509-529 ◽

Cited By ~ 2

Author(s):

Vanessa Paz Dennen ◽

Jennifer B. Myers ◽

Christie L. Suggs

Keyword(s):

Content Analysis ◽

Social Network ◽

Discourse Analysis ◽

Social Network Analysis ◽

Network Analysis ◽

Conversation Analysis ◽

Narrative Analysis ◽

Distributed Environments ◽

Web Based

In this chapter we examine how a variety of research approaches can be applied to the study of cross-blog interactions. Cross-blog interactions can be challenging to study because of they often require the researcher to reconsider traditional notions of temporality, discourse space, and conversation. Further, in many instances they are neither static nor well defined; defining the beginning and end of a discussion as well as locating all components of the discussion can be difficult. For this reason, we advocate a blend of six approaches (social network analysis, content analysis, discourse analysis, conversation analysis, narrative analysis, and ethnography). For each, we discuss strengths and limitations and provide examples of how the approach may be used to help fully capture the complexity of these interactions. Additionally we discuss web-based tools that are helpful when engaged in this type of research.

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