Algorithmically-driven social platforms present a challenge for self-presentation and identity management by obscuring audiences behind algorithmic mechanisms. Users are increasingly aware of this and actively adapting through folk theorization, but we do not know how users are coping with the constant change endemic to these platforms. We also do not know how we can assist users in coping with this change on an ongoing, extensible basis. This dissertation presents an exploratory look at these questions via a grounded theory study of an Asynchronous Remote Community with 25 everyday users of social platforms who, by virtue of being LGBTQ+, have heightened self-presentation concerns. Results highlight the importance of the level of complexity which one is theorizing about platforms, as well as the impact of user perceptions of the platform’s overall spirit on folk theorization and adaptation to change. This dissertation contributes a three-tier classification system for folk theorization, Folk Theorization Complexity Level (TCL), an updated concept of platform spirit as applied to social platforms, and a set of illustrative adaptation pathways which help us better understand differential adaptation behavior. Moreover, it argues that, in light of these findings, folk theorization is a promising path towards promoting a robust Algorithmic Literacy, with preliminary directions towards implementation.

Creator

• DeVito, Michael Ann

DOI

• https://doi.org/10.21985/n2-b9r4-3847

Subject

• Cognitive psychology
• Information science
• Computer science

Language

• en

Alternate Identifier

• etdadmin_upload_779819
• http://dissertations.umi.com/northwestern:15378

Keyword

• human computer interaction
• folk theorization
• lbgtq
• folk theories
• algorithmic literacy

Date created

• 2020-01-01

Resource type

• Dissertation

Rights statement

• In Copyright