by 
The last decade has seen the rise of new technologies for making information more broadly available and accessible. Variously called ‘user-generated content,’ ‘social media,’ ‘social news,’ ‘crowd-curation,’ and so on, these design conventions, algorithmic arrangements, and user practices have been widely praised for ‘democratizing’ media by lowering the barriers to publishing, accrediting, and aggregating information. Intermediary platforms like Facebook, reddit, and Twitter, among others, are generally expected to elicit valuable knowledge through the algorithmic filtering mechanisms broadly distributed among their users.
This thesis investigates user-generated censorship: an emergent mode of intervention by which users strategically manipulate social media to suppress speech. It shows that the tools designed to help make information more available have been repurposed and reversed to make it less available. Case studies reveal that these platforms, far from being neutral pipes through which information merely travels, are in fact contingent sociotechnical systems upon and through which users effect their politics through the power of algorithms. By strategically pulling the levers which make links to sites more or less visible, users recompose the representations of the world produced by social media, altering pathways of access and availability and changing the flow of information.
This thesis incorporates insights from media studies, sociology, law and policy, information science, and science-technology studies to study user-generated censorship. It contributes to a broader conversation now emerging across fields which seeks to explore and understand the politics of our developing social media systems.
