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EMOTIONS AND WELL-BEING IN TWITTER PROFESSIONAL DEVELOPMENT: A CONTENT ANALYSIS OF SOCIAL MEDIA DATA
|Title:||EMOTIONS AND WELL-BEING IN TWITTER PROFESSIONAL DEVELOPMENT: A CONTENT ANALYSIS OF SOCIAL MEDIA DATA|
|Authors:||Tanaka, Naomi Lisa Rombaoa|
|Contributors:||Sorensen Irvine, Christine (advisor)|
Learning Design and Technology (department)
show 3 moreSocial Networks
|Publisher:||University of Hawai'i at Manoa|
|Abstract:||This content analysis study explored how emotions and well-being were reflected in a social media professional development community for educators. Given the lack of research on the intersection of online professional development, educator well-being, content analysis of social media posts, online emotional contagion, and social network theory, this study filled a unique need. The research questions were: (1) What is the emotional sentiment of posts in a social media professional development community for educators? (2) How is well-being represented in posts in a social media professional development community for educators? (3) What is the nature of the social network that posts flow through? Public social media posts from a professional development community on Twitter were downloaded over the course of a week, cleaned, and analyzed. The first two research questions were answered by analyzing the tweets against publicly available lexicons especially designed to gain information about emotions, sentiment, and well-being in text data. A social network analysis of #edchat was conducted for the third research question. The key findings for question 1 were that positive emotions flowed through the network over negative emotions. The top occurring emotions were “anticipation,” “trust,” and “surprise.” For research question 2, it was discovered that the positive instances of well-being elements flowed through the network over their negative counterparts. The top occurring elements were “engagement” and “meaning.” Results of the social network analysis were that #edchat is a medium to large size well connected but not dense network. Several users were found to be connectors and bridges of information, emotions, and well-being flow throughout the network. This study demonstrated that emotions and well-being elements may be found in Twitter data through the use of research- based lexicons. Content analysis of large sample sizes of publicly available social media data is relatively cheap, easy, quick, and repeatable for almost real- time updates. Given that social network analysis can uncover key players in the network responsible for spreading and gatekeeping information, educators, policy makers, and instructional designers may learn how to best use the power and virality of social media for educational and professional development purposes.|
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||
Ph.D. - Learning Design and Technology|
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