The increased importance of opaque, algorithmically-driven social platforms (e.g., Facebook, YouTube) to everyday users as a medium for self-presentation effectively requires users to speculate on how platforms work in order to decide how to behave to achieve their self-presentation goals. This speculation takes the form of folk theorization. Because platforms constantly change, users must constantly re-evaluate their folk theories. Based on an Asynchronous Remote Community study of LGBTQ+ social platform users with heightened self-presentation concerns, I present an updated model of the folk theorization process to account for platform change. Moreover, I find that both the complexity of the user’s folk theorization and their overall relationship with the platform impact this theorization process, and present new concepts for examining and classifying these elements: theorization complexity level and perceived platform spirit. I conclude by proposing a folk theorization-based path towards an extensible algorithmic literacy that would support users in ongoing theorization.
Michael Ann DeVito. 2021. Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms. Proceedings of the ACM on Human-Computer Interaction, 5, CSCW2, Article 339, 35 pages. doi: 10.1145/3476080