Analyzing Social Book Reading Behavior on Goodreads and How It Predicts Amazon Best Sellers


A book’s success/popularity depends on various parameters: extrinsic and intrinsic. In this paper, we study how the book reading characteristics might influence the popularity of a book. Towards this objective, we perform a cross-platform study of Goodreads entities and attempt to establish the connection between various Goodreads entities and the popular books (“Amazon best sellers”). We analyze the collective reading behavior on Goodreads platform and quantify various characteristic features of the Goodreads entities to identify differences between these Amazon best sellers (ABS) and the other non-best-selling books. We then develop a prediction model using the characteristic features to predict if a book shall become a best seller after 1 month (15 days) since its publication. On a balanced set, we are able to achieve a very high average accuracy of 88.72% (85.66%) for the prediction where the other competitive class contains books which are randomly selected from the Goodreads dataset. Our method primarily based on features derived from user posts and genre-related characteristic properties achieves an improvement of 16.4% over the traditional popularity factor (ratings, reviews)-based baseline methods. We also evaluate our model with two more competitive sets of books (a) that are both highly rated and have received a large number of reviews (but are not best sellers) (HRHR) and (b) Goodreads Choice Awards Nominated books which are non-best sellers (GCAN). We are able to achieve quite good results with very high average accuracy of 87.1% as well as high ROC for ABS vs GCAN. For ABS vs HRHR, our model yields a high average accuracy of 86.22%.

This work has been published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 and a preprint version can be accessed from Preprint Link.