Datensatz

Zusammenfassung

Detecting the forces behind the success or failure of cultural products, such as books or films, remains a challenge. Three such forces are drift, context-biased selection, and selection based on content—when things succeed because of their intrinsic appeal. We propose a tool to study content-biased selection in sets of cultural collections—e.g. libraries or movie collections — based on the “shortlist effect”: the fact that smaller collections are more selective, more likely to favour highly appealing items over others. We use a model to show that, when the shortlist effect is at work, content-biased cultural selection is associated with greater nestedness in sets of collections. Having established empirically the existence of the shortlist effect, and of content-biased selection, in 28 sets of movie collections, we show that nestedness contributions can be used to estimate to what extent specific movies owe their success to their intrinsic properties. This method can be used in a wide range of datasets to detect the items that owe their success to their intrinsic appeal, as opposed to “hidden gems” or “accidental hits”.