Understanding Book Popularity on Goodreads

TL;DR

This paper investigates whether the popularity of books on Goodreads can be predicted from various features, achieving high accuracy and highlighting user engagement and author prestige as key factors.

Contribution

It demonstrates the feasibility of predicting Goodreads book popularity using multiple features with significant accuracy, emphasizing the importance of engagement and prestige.

Findings

Prediction correlation coefficient ~0.61

Low RMSE (~1.25) indicates accurate predictions

User engagement and author prestige are crucial factors

Abstract

Goodreads has launched the Readers Choice Awards since 2009 where users are able to nominate/vote books of their choice, released in the given year. In this work, we question if the number of votes that a book would receive (aka the popularity of the book) can be predicted based on the characteristics of various entities on Goodreads. We are successful in predicting the popularity of the books with high prediction accuracy (correlation coefficient ~0.61) and low RMSE (~1.25). User engagement and author's prestige are found to be crucial factors for book popularity.