Cecilia Aragon

Autumn 2018

Distributed Mentoring and Fanfiction Data Analytics

Are you interested in applying human-centered data science to study how people learn from online fandom?

This ongoing research project studies informal learning in online fanfiction communities. We are looking for students with experience in either (a) programming and analysis of large text datasets or (b) qualitative research in online fandoms, to join an existing research group. We have published multiple papers on our research and are in the process of submitting others.

We have found quantitative and qualitative evidence that distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project continues our efforts with a specific focus on visual analytics of a large dataset. We’ve collected a vast, rich text dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites and have applied both qualitative (ethnography) and quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level).

I am looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596. To apply, please send an email to Cecilia Aragon ( including the following:

  • A few paragraphs describing why you are interested in the project and your experience in programming, quantitative and/or qualitative research, and fandom participation.
  • Your resume and unofficial copy of your transcript.
  • The number of credit hours you are seeking (2-5).
  • [Optional] A code sample you wrote on your own, not for class, and preferably for web scraping and text processing. Github commits a plus!

Meeting times are TBD and will be scheduled for the convenience of all participants.


Cecilia Aragon's Directed Research Group archive: