Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments
This addresses the problem of understanding literary quality judgments for researchers in computational humanities, but it is incremental as it builds on prior work like the Riddle of Literary Quality project.
The study investigated whether literary quality perceptions stem from textual features or social factors by comparing ratings from a controlled experiment with 48 participants to survey data and machine learning predictions, finding moderate to strong correlations with survey ratings but predictions were closer to survey ratings.
It is an open question to what extent perceptions of literary quality are derived from text-intrinsic versus social factors. While supervised models can predict literary quality ratings from textual factors quite successfully, as shown in the Riddle of Literary Quality project (Koolen et al., 2020), this does not prove that social factors are not important, nor can we assume that readers make judgments on literary quality in the same way and based on the same information as machine learning models. We report the results of a pilot study to gauge the effect of textual features on literary ratings of Dutch-language novels by participants in a controlled experiment with 48 participants. In an exploratory analysis, we compare the ratings to those from the large reader survey of the Riddle in which social factors were not excluded, and to machine learning predictions of those literary ratings. We find moderate to strong correlations of questionnaire ratings with the survey ratings, but the predictions are closer to the survey ratings. Code and data: https://github.com/andreasvc/litquest