UCLA

The rise of online platforms for buying and discussing books such as Amazon and Goodreads opens up new possibilities for reception studies in the twenty-first century. These platforms allow readers unprecedented freedom to preview and talk to others about books, but they also exercise unprecedented control over which books readers buy and how readers respond to them. Online reading platforms rely on algorithms with implicit assumptions that at times imitate and at times differ from the conventions of literary scholarship. This dissertation interrogates those algorithms, using computational methods including machine learning and natural language processing to analyze hundreds of thousands of online book reviews in order to find moments when literary and technological perspectives on contemporary reading can inform each other. A focus on the algorithmic logic of bookselling allows this project to critique the ways companies sell and recommend books in the twenty-first century, while also making room for improvements to these algorithms in both accuracy and theoretical sophistication. This dissertation forms the basis of a re-imagining of literary scholarship in the digital age that takes into account the online platforms that mediate so much of our modern literary consumption.