Discovering Latent Patterns from the Analysis of User-Curated Movie Lists

TL;DR

This paper analyzes user-curated IMDb movie lists using network analysis to uncover latent movie groupings that go beyond traditional categories like genre or director.

Contribution

It introduces a novel approach to mine and analyze crowdsourced list data to discover hidden patterns in movies.

Findings

Uncovered nuanced movie groupings through co-listing analysis

Demonstrated the effectiveness of network analysis on user-curated data

Revealed latent relationships not captured by standard metadata

Abstract

User content curation is becoming an important source of preference data, as well as providing information regarding the items being curated. One popular approach involves the creation of lists. On Twitter, these lists might contain accounts relevant to a particular topic, whereas on a community site such as the Internet Movie Database (IMDb), this might take the form of lists of movies sharing common characteristics. While list curation involves substantial combined effort on the part of users, researchers have rarely looked at mining the outputs of this kind of crowdsourcing activity. Here we study a large collection of movie lists from IMDb. We apply network analysis methods to a graph that reflects the degree to which pairs of movies are "co-listed", that is, assigned to the same lists. This allows us to uncover a more nuanced grouping of movies that goes beyond categorisation schemes based on attributes such as genre or director.