What Does Twitter Say About Self-Regulated Learning? Mapping Tweets From 2011 to 2021 - Khalil, Mohammad; Belokrys, Gleb (Frontiers in Psychology, 2022)
Social network services such as Twitter are important venues that can be used as rich data sources to mine public opinions about various topics. In this study, we used Twitter to collect data on one of the most growing theories in education, namely Self-Regulated Learning (SRL) and carry out further analysis to investigate What Twitter says about SRL?. This work uses three main analysis methods, descriptive, topic modelling, and geocoding analysis. The searched and collected dataset consists of a large volume of relevant SRL posts equal to 54,070 tweets between 2011 and 2021. The descriptive analysis uncovers a growing discussion on SRL on Twitter from 2011 till 2018 and then markedly decreased till the collection day. For topic modelling, the text mining technique of Latent Dirichlet allocation was applied and revealed insights on computationally processed topics. Finally, the geocoding analysis uncovers a diverse community from all over the world, yet a higher density representation of users from the Global North was identified. Further implications are discussed in the paper.