Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

要旨

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 which would support users in ongoing theorization.

受賞
Honorable Mention
著者
Michael Ann DeVito
University of Colorado Boulder, Boulder, Colorado, United States
論文URL

https://doi.org/10.1145/3476080

動画

会議: CSCW2021

The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing

セッション: Interpreting and Explaining AI

Papers Room B
8 件の発表
2021-10-26 19:00:00
2021-10-26 20:30:00