Presentation description

Determining how to categorize works into genres often leads to debates. Traditionally, when discussing genres, we think of categories such as Fantasy, Mystery, Romance, etc. This research explores genres within fan-fiction by analyzing generic tags-tags that indicate overall themes or types of content in a story-through a data science approach.

We construct a network of popular generic tags from Archive of Our Own (AO3), a platform for sharing and reading fan-fiction. Using community detection methods, we identify clusters of tags that frequently co-occur in the same story. Additionally, we build another network to examine the connections between these tag clusters.

Generic tags are crucial for understanding how genres are organized in the fan-fiction community, which tends to be more complex and nuanced compared to traditional genres. This technology-driven method may reveal new genre forms distinct from conventional categories. Our findings provide insights into genre dynamics within this female/LGBTQIA+ centered community, uncovering patterns that warrant further exploration. These insights could be valuable for future researchers seeking to better understand fan-fiction community behaviors.