Class
Article
College
Emma Eccles Jones College of Education and Human Services
Department
Instructional Technology and Learning Sciences Department
Faculty Mentor
Lisa Lundgren
Presentation Type
Oral Presentation
Abstract
Characterizing who participates in and contributes to conversations with online social worlds can help extend our understanding of the science education ecosystem, including giving insight into what kinds of communication works for whom and under what conditions. We describe methods for characterizing an online, scientific affinity space using three science-based hashtags: #ScienceTwitter, #AcademicTwitter, and #SciComm. Researchers applied an established framework to describe a subset of users who interacted with these hashtags (n = 1000) and analyzed the structure of the network during a one-month period. Social network analysis showed a highly dispersed network, but equal numbers of scientists, members of the public, and educators all equally controlling information flow. Using social network analysis and describing users with the social network gives a deeper understanding of the community and interactions within the space, which can lead to the development of better science communication efforts in the digital realm as well as establish a better corpus of user data for future efforts using machine learning to more quickly and accurately describe large communities.
Location
Logan, UT
Start Date
4-12-2023 10:30 AM
End Date
4-12-2023 11:30 AM
Included in
Social Network Analysis Shows Equal Numbers of Public, Educators, and Scientists Within an Online Social World
Logan, UT
Characterizing who participates in and contributes to conversations with online social worlds can help extend our understanding of the science education ecosystem, including giving insight into what kinds of communication works for whom and under what conditions. We describe methods for characterizing an online, scientific affinity space using three science-based hashtags: #ScienceTwitter, #AcademicTwitter, and #SciComm. Researchers applied an established framework to describe a subset of users who interacted with these hashtags (n = 1000) and analyzed the structure of the network during a one-month period. Social network analysis showed a highly dispersed network, but equal numbers of scientists, members of the public, and educators all equally controlling information flow. Using social network analysis and describing users with the social network gives a deeper understanding of the community and interactions within the space, which can lead to the development of better science communication efforts in the digital realm as well as establish a better corpus of user data for future efforts using machine learning to more quickly and accurately describe large communities.