What factors influence the popularity of user-generated text in the creative domain? A case study of book reviews
This work addresses the problem of understanding what makes user-generated content popular in creative domains like book reviews, but it is incremental as it primarily reports negative or limited findings without major breakthroughs.
The study investigated factors influencing the popularity of book reviews by analyzing psychological, lexical, semantic, and readability features, finding that most attributes, except for a few like review length and emotions, did not significantly differ between popular and non-popular groups, and machine learning classifiers performed poorly in predicting popularity.
This study investigates a range of psychological, lexical, semantic, and readability features of book reviews to elucidate the factors underlying their perceived popularity. To this end, we conduct statistical analyses of various features, including the types and frequency of opinion and emotion-conveying terms, connectives, character mentions, word uniqueness, commonness, and sentence structure, among others. Additionally, we utilize two readability tests to explore whether reading ease is positively associated with review popularity. Finally, we employ traditional machine learning classifiers and transformer-based fine-tuned language models with n-gram features to automatically determine review popularity. Our findings indicate that, with the exception of a few features (e.g., review length, emotions, and word uniqueness), most attributes do not exhibit significant differences between popular and non-popular review groups. Furthermore, the poor performance of machine learning classifiers using the word n-gram feature highlights the challenges associated with determining popularity in creative domains. Overall, our study provides insights into the factors underlying review popularity and highlights the need for further research in this area, particularly in the creative realm.