OTW Guest Post

OTW Guest Post: Smitha Milli

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Smitha Milli is a 4th-year undergraduate at UC Berkeley whose research interests lie in artificial intelligence and cognitive science. Today, Smitha talks about her research using natural language processing to reveal patterns in fanfiction texts, the results of which is available online.

How did you come to work with fanfiction in your research?

At the time I started this project my main research focus was in natural language processing (NLP). Natural language processing is a subfield of artificial intelligence that is concerned with creating algorithms to process and understand language. If you’ve ever used Google Translate or Siri, you’ve used products that depend on NLP research!

In addition to having many commercial applications, NLP can also be used as a tool to explore literature. People have automatically tracked dynamic relationships between characters, created computational models of literary character, and analyzed the change in emotional content over the course of a story. However, a bottleneck to improving algorithms in the literary domain was the lack of a large-scale dataset of modern literature. I originally started looking into fanfiction as a source for this kind of data. As I looked further, I found that the structure of fanfiction also made it possible to define interesting, new problems for NLP and I became interested in computationally analyzing social science questions about fanfiction.

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