Shadowbanning is a unique content moderation strategy receiving recent media attention for the ways it impacts marginalized social media users and communities. Social media companies often deny this content moderation practice despite user experiences online. In this paper, we use qualitative surveys and interviews to understand how marginalized social media users make sense of shadowbanning, develop folk theories about shadowbanning, and attempt to prove its occurrence. We find that marginalized social media users collaboratively develop and test algorithmic folk theories to make sense of their unclear experiences with shadowbanning. Participants reported direct consequences of shadowbanning, including frustration, decreased engagement, the inability to post specific content, and potential financial implications. They reported holding negative perceptions of platforms where they experienced shadowbanning, sometimes attributing their shadowbans to platforms' deliberate suppression of marginalized users' content. Some marginalized social media users acted on their theories by adapting their social media behavior to avoid potential shadowbans. We contributecollaborative algorithm investigation : a new concept describing social media users' strategies of collaboratively developing and testing algorithmic folk theories. Finally, we present design and policy recommendations for addressing shadowbanning and its potential harms.

The Internet has, for several decades, played a critical role in autistic self-advocacy and community building. This semi-autoethnographic, interpretivist study turns to #ActuallyAutistic Twitter to examine autistic concerns about autism research, how these concerns differ from those of autism researchers, and how autistics interact with autism research and researchers. I find that #ActuallyAutistic Twitter discourses align with the neurodiversity paradigm, while dominant autism discourses in the academy align with the medical model of disability. Though both orientations towards autism research sometimes share research priorities, they represent fundamentally irreconcilable approaches to these priorities and autism, more broadly. I explore how autistics on Twitter interact with non-autistic researchers and how the tenor of these interactions varies according to which research paradigm a particular researcher subscribes. I conclude with a discussion of how HCI researchers interested in autism can operationalize these findings by approaching their work through the framework of crip technoscience.