Development of ClassNet, a Web-Based Social Network Analysis Software Program

2017 ◽  
Vol 48 (3) ◽  
pp. 107-137
Author(s):  
Jonghyo Park ◽  
Kyoungsuk Moon ◽  
Haijeong Ahn ◽  
Jiyoung Choi ◽  
Kyungwha Hong ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Analysis Software ◽  
Web Based ◽  
Software Program

scholarly journals Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

10.2196/24690 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24690
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Web Based ◽  
Network Characteristics ◽  
Using Data

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.

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

2015 ◽  
Vol 6 (1) ◽  
pp. 1-23 ◽  
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.

Spatiotemporal Network Analysis and Visualization

2015 ◽  
Vol 6 (2) ◽  
pp. 77-97 ◽  
Author(s):  
Judith Gelernter ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Third Party ◽  
Analysis Software ◽  
Time Intervals ◽  
External Resources ◽  
Newspaper Articles

Spatiotemporal social network analysis shows relationships among people at a particular time and location. This paper presents an algorithm that mines text for person and location words and creates connections among words. It shows how this algorithm output, when chunked by time intervals, may be visualized by third-party social network analysis software in the form of standard network pin diagrams or geographic maps. Its data sample comes from newspaper articles concerning the 2006 Darfur crisis in Sudan. Given an immense data sample, it would be possible to use the algorithm to detect trends that would predict the next geographic center(s) of influence and types of actors (foreign dignitaries or domestic leaders, for example). This algorithm should be widely generalizable to many text domains as long as the external resources are modified accordingly.

Researching Community in Distributed Environments

2011 ◽  
pp. 509-529 ◽  
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.

Spatiotemporal Network Analysis and Visualization

2016 ◽  
pp. 373-395
Author(s):  
Judith Gelernter ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Third Party ◽  
Analysis Software ◽  
Time Intervals ◽  
External Resources ◽  
Newspaper Articles

Spatiotemporal social network analysis shows relationships among people at a particular time and location. This paper presents an algorithm that mines text for person and location words and creates connections among words. It shows how this algorithm output, when chunked by time intervals, may be visualized by third-party social network analysis software in the form of standard network pin diagrams or geographic maps. Its data sample comes from newspaper articles concerning the 2006 Darfur crisis in Sudan. Given an immense data sample, it would be possible to use the algorithm to detect trends that would predict the next geographic center(s) of influence and types of actors (foreign dignitaries or domestic leaders, for example). This algorithm should be widely generalizable to many text domains as long as the external resources are modified accordingly.

scholarly journals POLA INTERAKSI DAN AKTOR YANG PALING BERPERAN PADA EVENT JGTC 2013 MELALUI MEDIA SOSIAL TWITTER (STUDI MENGGUNAKAN METODE SOCIAL NETWORK ANALYSIS)

2017 ◽  
Vol 14 (3) ◽  
pp. 201 ◽  
Author(s):  
Rio Oktora ◽  
Andry Alamsyah
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Analysis Software ◽  
Social Media Monitoring ◽  
Media Monitoring ◽  
Programming Interface

Selama beberapa tahun terakhir, internet telah berkembang dengan cepat seiring dengan perkembangan teknologi. Data percakapan yang terdapat di media sosial dapat dimanfaatkan untuk melihat pola interaksi dan aktor yang paling berperan pada event JGTC 2013 melalui media sosial Twitter. Penelitian ini memanfaatkan big data dari media sosial Twitter yang diperoleh dari Twitter melalui API (Aplication Programming Interface) dengan bantuan teknis dari NoLimitID (perusahaan social media monitoring & analytic tools). Data tersebut kemudian diolah dengan pendekatan Social Network Analysis. Software yang digunakan untuk menghitung dan menvisualisasikan hasil analysis adalah Gephi. Penentuan aktor yang berperan dalam event JGTC 2013 dihitung berdasarkan centrality yang terdiri dari degree centrality, betweenness centrality, closeness centrality, dan eigenvector centrality. Sampel dalam penelitian ini adalah tweet yang berupa interaksi (terdapat mention, baik berupa reply maupun qoute retweet) yang memuat kata 'JGTC' dan '#JGTC36' pada 1 Desember 2013. Hasil penelitian pada event JGTC 2013 terdapat 7624 node (akun) yang terlibat dengan 7445 edge (interaksi) yang terjadi di network tersebut. Aktor (node) yang paling berpengaruh dalam network JGTC secara keseluruhan adalah raisa6690 yang merupakan bintang tamu pengisi acara event JGTC 2013

Sign in / Sign up

Export Citation Format

Share Document