Folk Theorization, Platform Spirit, and Adaptation with AI-Driven Social Systems

AI-based platforms are an increasingly important part of our social lives. However, they are currently the equivalent of an unpredictable friend who acts in a confusing, often harmful manner, and refuses to explain themselves. They are a friend that does not deserve upfront trust, and with whom users question continuing their relationship. However, they are still an important friend, as platforms such as Facebook and TikTok have become essential to accomplishing user goals. That leaves users to investigate and speculate on how the system works and how to deal with it to accomplish their goals. 

I study folk theorization, the process by which users form informal, socially-informed, quasi-causal understandings of how platforms function which guide users in on-platform decision-making . Folk theories operate at the user level, and focus on existing user understanding, enabling me to faithfully capture the motivations behind emergent behavior as well as the informal, often emotionally-charged relationships between users and platforms.  Essentially, studying folk theorization allows me to examine how users attempt to communicate with, understand, and adapt to their problematic friend – and when, why, and how their relationship to said friend motivates them to give up on trying. 

Publications

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

Michael Ann DeVito. 2021. Proceedings of the ACM on Human-Computer Interaction, 5, CSCW2, Article 339.

How People Form Folk Theories of Social Media Feeds and What it Means for How We Study Self-Presentation

Michael Ann DeVito, Jeremy Birnholtz, Jeffery T. Hancock, Megan French, and Sunny Liu. 2018. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.

“Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media

Michael Ann DeVito, Darren Gergle, and Jeremy Birnholtz. 2017. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3163–3174.

Platforms, People, and Perception: Using Affordances to Understand Self-Presentation on Social Media

Michael Ann DeVito, Jeremy Birnholtz, and Jeffery T. Hancock. 2017. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17), 740–754.


Organizing and Agenda-Setting Work

“This Seems to Work”: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor

Lindsey Cameron, Angele Christin, Michael Ann DeVito, Tawanna R. Dillahunt, Madeleine Elish, Mary Gray, Rida Qadri, Noopur Raval, Melissa Valentine, and Elizabeth Anne Watkins. 2021. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, Article 115.

The Algorithm and the User: How Can HCI Use Lay Understandings of Algorithmic Systems?

Michael Ann DeVito, Jeffrey T. Hancock, Megan French, Jeremy Birnholtz, Judd Antin, Karrie Karahalios, Stephanie Tong, and Irina Shklovski. 2018. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA ’18), Paper panel04.