Revisiting the relevance of traditional genres: a network analysis of fiction readers' preferences

TL;DR

This study uses network analysis of Goodreads data to evaluate how well traditional fiction genres align with actual reader preferences, revealing nuanced community structures and proposing a new classification based on maturity and realism.

Contribution

It introduces a network-based approach to analyze reader preferences and proposes a novel two-factor classification system for fiction stories.

Findings

Network communities align with traditional genre combinations.

Reader enjoyment and reading patterns form different community structures.

A two-factor model explains most variance in book preferences.

Abstract

We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.