Fostering Self-Directed Learning with Social Software: Social Network Analysis and Content Analysis

2008 ◽  
pp. 203-215 ◽  
Author(s):  
Effie Lai-Chong Law ◽  
Anh Vu Nguyen-Ngoc
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Software ◽  
Self Directed Learning ◽  
Directed Learning ◽  
Software Social

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.

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.

A Methodological Approach for Blended Communities

2011 ◽  
pp. 103-121 ◽  
Author(s):  
Susan Annese ◽  
Marta Traetta
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Quantitative Methods ◽  
Qualitative Content Analysis ◽  
Methodological Approach ◽  
Qualitative And Quantitative Methods ◽  
Mixed Approach ◽  
Methodological Reflection

The current diffusion of blended communities, characterized by the integration of online and offline interactions, has made necessary a methodological reflection about the suitable approaches to explore psychosocial dynamics in virtual and real communities. In this chapter we propose a mixed approach that ‘blends’ qualitative and quantitative methods: by combining qualitative content analysis with Social Network Analysis we investigate participation dynamics and by employing this methodological combination in an original way we create an innovative method, called Positioning Network Analysis, to examine identity dynamics. We will describe the characteristics of this methodological device, providing some examples in order to show the manifold use of these original tools.

The manifest and latent functions of Twitter use by journalists: An observational study among economic journalists

2019 ◽  
Vol 8 (3) ◽  
pp. 311-329
Author(s):  
Michiel Johnson ◽  
Steve Paulussen ◽  
Peter Van Aelst
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Observational Study ◽  
Dominant Position ◽  
Automated Content Analysis ◽  
The Social ◽  
Potential Sources ◽  
The Media

This study focuses on Twitter use among economic journalists working for print media in Belgium. By looking into their tweeting and following behaviour, the article examines how economic journalists use Twitter for promotional, conversational and sourcing purposes. Based on an automated content analysis of what they tweet and a social network analysis of whom they follow, the results show that economic journalists mainly use Twitter to promote themselves and their news organization rather than to engage in public conversation on the platform. In addition, the study looks into their following behaviour to investigate which actors they consider as 'potential sources'. Here, the findings are consistent with previous studies among political and health journalists, indicating that journalists are more likely to follow institutionally affiliated rather than non-affiliated sources on Twitter. Furthermore, the social network analysis gives additional evidence of the media-centered of journalists' Twitter use, as media-affiliated actors maintain a dominant position in the economic journalists' Twitter networks.

scholarly journals How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhijun Wang ◽  
Terry Anderson ◽  
Li Chen
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
Data Output ◽  
Participation Inequality ◽  
Interaction Traces ◽  
Insight Into ◽  
Interpretative Research

<p class="3">In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs’ hashtag (#Change 11). This data was analysed using content analysis and social network analysis within an interpretative research paradigm. The content analysis was used to examine the technology learners used to support their learning while the social network analysis focused on the participant in different spaces and their participation patterns in connectivist learning.</p><p class="3">The findings from this research include: 1) A variety of technologies were used by learners to support their learning in this course; 2) Four types of participation patterns were reveled, including unconnected floaters, connected lurkers, connected participants, and active contributors. The participation of learners displays the participation inequality typical of social media, but the ratio of active contributors is much higher than xMOOCs; 3) There were five basic structures of social networks formed in the learning; and 4) The interaction around topics and topic generation supports the idea of learning as network creation after the analysis of participation patterns that are based on some deep interactive topic. The aim of this study is to gain insight into the behaviors of learners in a cMOOC in an open and distributed online environment, so that future MOOCs designers and facilitators can understand, design and facilitate more effective MOOCs for learners.</p>

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