Below are posts associated with the “computational text analysis” method.
Pseudonymous academics: Authentic tales from the Twitter trenches
Academics’ use of social media platforms is widely recognized and often understood as an extension of traditional academic practice. However, this understanding does not account for academics’ use of pseudonymous Twitter accounts. We used a combination of computational and human-driven methods to examine the activity of 59 anonymized, self-identified academics on Twitter. Our computational analysis identified five broad topics: discussing academic life, discussing British news and affairs, discussing everyday life, surviving lockdown, and engaging with academic Twitter. Within these broad topics, we identified 24 more specific codes, most of which were concentrated in individual topics, with some cross-cutting codes. These codes demonstrate how the pseudonymous accounts considered in this study can be considered ‘authentically academic’ even if they do not conform with widespread expectations of academic social media use.
Identifying multiple learning spaces within a single teacher-focused Twitter hashtag
The existing work on teacher-focused Twitter hashtags typically frames each hashtag as a single, unified phenomenon, thereby collapsing or erasing differences between them (and any resulting implications for learning). In this study, we conceived of teacher-focused hashtags as affinity spaces potentially containing subspaces distinguished by synchronous chats and other, asynchronous communication. We used computational methods to explore how participation differed in terms of content, interactions, and portals between these contexts within the #michED hashtag used by Michigan teachers. During the 2015–2016 academic year, #michED saw more non-chat activity than chat activity, and most participants only engaged in one mode of activity or the other. Participation during chats was associated with more replying as well as more socially-, affectively-, and cognitively-related content, suggesting a focus on social interaction. In contrast, non-chat participation was associated with more retweeting, mentioning, hyperlinks, and hashtags, suggesting a focus on content dissemination. These results suggest that different affinity spaces—and different literacy practices—may exist within the same hashtag to support different objectives. Teachers, teacher educators, and researchers should therefore be careful to make these distinctions when considering Twitter as a learning technology for teachers.