TY - GEN
T1 - Leveraging Influencer Groups to Examine Learning Networks on Twitter
AU - Bowles, Stephanie
AU - Sharma, Priya
N1 - Publisher Copyright:
© 2023 International Society of the Learning Sciences (ISLS). All rights reserved.
PY - 2023
Y1 - 2023
N2 - Our study builds on theories emphasizing connectivity for learning on social media to study learning networks on Twitter (e.g., Haythornthwaite, 2019; Siemens, 2005). Considering the nature of power law distribution on social media (Yoshida, 2021), we focus on a group of influencers from the Non-Fungible Token (NFT) community to uncover online learning networks and their emergent practices. By employing an exploratory Social Network Analysis (Nooraie et al., 2020) and qualitative analysis, we examine the way in which influencers form learning relations through conversational interactions and content, and the modalities and discursive strategies used in their tweets. We find that influencers built a strong learning network through sustained interactions and content relations among themselves and with a wide community outreach. This analysis contributes to the way that learning networks are analyzed and found on Twitter.
AB - Our study builds on theories emphasizing connectivity for learning on social media to study learning networks on Twitter (e.g., Haythornthwaite, 2019; Siemens, 2005). Considering the nature of power law distribution on social media (Yoshida, 2021), we focus on a group of influencers from the Non-Fungible Token (NFT) community to uncover online learning networks and their emergent practices. By employing an exploratory Social Network Analysis (Nooraie et al., 2020) and qualitative analysis, we examine the way in which influencers form learning relations through conversational interactions and content, and the modalities and discursive strategies used in their tweets. We find that influencers built a strong learning network through sustained interactions and content relations among themselves and with a wide community outreach. This analysis contributes to the way that learning networks are analyzed and found on Twitter.
UR - https://www.scopus.com/pages/publications/85183917910
UR - https://www.scopus.com/pages/publications/85183917910#tab=citedBy
U2 - 10.22318/cscl2023.536194
DO - 10.22318/cscl2023.536194
M3 - Conference contribution
AN - SCOPUS:85183917910
T3 - Proceedings of International Conference of the Learning Sciences, ICLS
SP - 265
EP - 268
BT - ISLS Annual Meeting 2023
A2 - Damsa, Crina
A2 - Borge, Marcela
A2 - Koh, Elizabeth
A2 - Worsley, Marcelo
PB - International Society of the Learning Sciences (ISLS)
T2 - 16th International Conference on Computer-Supported Collaborative Learning, CSCL 2023
Y2 - 10 June 2023 through 15 June 2023
ER -