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.

scholarly journals Gossip at one’s fingertips: Predictors of celebrity news on Twitter

Journalism ◽  
2018 ◽  
Vol 21 (5) ◽  
pp. 707-726
Author(s):  
Yan Yan ◽  
Wanjiang Zhang
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
News Coverage ◽  
Capitalist Society ◽  
Twitter Account ◽  
Celebrity News

The present study collected 2223 tweets of news about the Top 100 celebrities from People Magazine’s Twitter account during the year 2016. A combination of content analysis and social network analysis was used to examine celebrity attributes, news features, and the relationships between celebrities and news topics. Results indicated that news agendas and audiences’ responses were highly different. News coverage was primarily determined by news features, yet audiences cared only about big stars. Regular topics centered on the themes of celebrity news. The celebrity-by-topic network was topic-driven rather than human-driven, demonstrating the nature of the celebrity industry as an embodiment of capitalist society.

Peer Assistance and Interaction in an Online Forum

2011 ◽  
pp. 118-130
Author(s):  
Ugur Kale
Keyword(s):  
Content Analysis ◽  
Instructional Design ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Peer Interaction ◽  
Graduate Level ◽  
Online Forum ◽  
Peer Assistance ◽  
To Receive

This study examines peer interaction and peer assistance observed in on an online forum, part of a graduate level instructional design course during the 2008 spring academic term. It incorporates both content analysis and social network analysis techniques. The content analysis results showed that the four types of peer assistance adopted from an existing framework were adequate to categorize the peer assistance that the students received during the study. Students tended to receive more Reflective assistance from their peers if their reading reflections provided high relevance to the course projects. Social network analysis results revealed that while 70% of the students provided peer assistance to one another, they were less likely to go beyond the course requirement of posting toward to end of the semester. Also, a further analysis demonstrated how SNA approach may help examine the influences of actor attributes on their observed communication.

scholarly journals COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

10.2196/19458 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e19458 ◽  
Author(s):  
Wasim Ahmed ◽  
Josep Vidal-Alaball ◽  
Joseph Downing ◽  
Francesc López Seguí
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
United Kingdom ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Conspiracy Theory ◽  
Fake News ◽  
The United Kingdom ◽  
Twitter Data

Background Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. Objective The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. Methods This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

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.

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