Unmasking the Wordsmith: Revealing Author Identity through Reader Reviews
Authors: Chiara Alzetta, Felice Dell'Orletta, Chiara Fazzone, Alessio Miaschi, Giulia Venturi (CNR-ILC, 2023)
Topic: Textual Genres & Literature Linguistics

Abstract
Traditional genre-based approaches for book recommendations face challenges due to the vague definition of genres. To overcome this, we propose a novel task called Book Author Prediction, where we predict the author of a book based on user-generated reviews’ writing style. To this aim, we first introduce the ‘Literary Voices Corpus’ (LVC), a dataset of Italian book reviews, and use it to train and test machine learning models. Our study contributes valuable insights for developing user-centric systems that recommend leisure readings based on individual readers’ interests and writing styles.

Introduction
Reading for pleasure is currently experiencing a significant decline, as evidenced by surveys indicating that leisure reading has reached an unprecedented low. Book recommender systems have been proposed as a valuable tool to promote the practice of reading for pleasure. These systems provide personalized suggestions and aid users in navigating the vast array of available literary works. Their integration into e-commerce services has long been explored, as it benefits both sellers and consumers. Typically integrated with online platforms, book recommender systems rely on the history of users to predict their future interests and provide recommendations based on the literary genre or authors that users have previously engaged with. While recommending the other books by an author that the reader enjoyed is trivial, suggesting books belonging to the same genre remains a complex area of study, particularly concerning literary novels. This is mostly due to the fact that the notion of genre represents a quite heterogeneous object of study due to multiple factors. In fact, the same book can be assigned to more than one literary genre either on the same reading platform or across diverse platforms. Accordingly, various approaches have been proposed to automatically identify literary genres using book content, titles or summaries, and even cover designs. Nevertheless, these models often face challenges when book content is inaccessible due to licensing restrictions. Consequently, an alternative and promising line of research on book recommender systems involves leveraging user reviews as a valuable source of information for generating recommendations. Analyzing reviews allows for a unique perspective on books from the viewpoint of their readers, without requiring access to their content. Reviews offer valuable insights into readers’ opinions and preferences, and they have been effectively utilized to predict trends in the book market. There are few attempts to exploit user reviews also for literary genre identification. These include [16] and [17] for English and Portuguese book reviews respectively. We have also contributed to this line of research by focusing on Italian book reviews. In our previous work, we demonstrated how book reviews published by amateur readers on two social reading platforms, namely Amazon and Goodreads, can be exploited to automatically identify the genre of the reviewed book. Building upon our prior investigations, our current research aims to explore whether the writing style of user-generated reviews, analyzed in terms of lexical and (morpho-)syntactic characteristics, can serve as a reliable source of information also to predict the author of a reviewed book. We started from the assumption that the vague definition of literary genres might make recommendations based on related authors more effective than genre-based approaches. To this end, inspired by the literature on Authorship Attribution, we introduced a novel task named Book Author Prediction. We tackle the problem as a supervised classification task, where the objective is to predict the author of a given book from a set of potential candidates. It is important to note that, unlike the traditional Authorship Attribution task, our information source consists of user-generated reviews rather than the books authored by the novelists themselves. This distinction adds a layer of complexity to the task, making it particularly challenging and novel in its approach. As a crucial step towards this objective, we introduce a novel dataset of Amazon and Goodreads book reviews, the ‘Literary Voices Corpus’ (LVC). The dataset successfully served in diverse experimental settings we explored in this work aimed at training and testing pretrained and traditional machine learning models, that use different configurations of lexical and (morpho-)syntactic features, to accomplish the new prediction task. The work presented in this study falls within the context of collective efforts to foster the habit of reading and enlarge the readership across different target audiences. Among these initiatives, LettERE (Letture pER TE) is a project that aims to encourage and promote the practice of reading by creating a reading recommendation system that provides personalised recommendations tailored to the reader’s language skills and interests (see Acknowledgements). In this regard, the research presented in this paper contributes significantly to the LettERE project’s objectives by showing that user-generated reviews can be effectively used to identify readers sharing common interests and ultimately provide personalised book recommendations. The remainder of the paper is organised as follows. Section 2 presents LVC, the novel collection of Italian book reviews referring to the books of six popular authors. Section 3 introduces the Book Author Prediction task and details the methodology and models exploited in this work to address it. Section 4 presents the results of our experiments. Finally, Section 5 offers conclusions and outlines potential future research directions.

Conclusion
This paper has explored an innovative approach that leverages user reviews as a source of information for Book Author Prediction. Building upon our prior work, we introduced a novel dataset of Amazon and Goodreads book reviews, LVC, which has been used for training and evaluating machine learning models addressing the novel book author prediction task. Our findings highlight the challenging nature of predicting the author of a novel from a reader’s review. However, the analysis of erroneous predictions pointed us to cases of books sharing a similar readership. This observation supports the intuition that user-generated reviews can effectively serve as a basis for personalized book recommendations. By analyzing reviews, we gained insights into readers’ preferences beyond the writing style of the book’s author, opening up new avenues for more tailored and user-centric recommendations. Moving forward, this research could be expanded by investigating the impact of exploiting user judgments as an additional feature for classification. Furthermore, the sentiment expressed by readers about a book, whether positive or negative, could be leveraged to validate and fine-tune personalized recommendations.