  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.
