Datasets and Models for Authorship Attribution on Italian Personal Writings
Authors: Gaetana Ruggiero, Albert Gatt, Malvina Nissim (University of Malta, University of Groningen, 2020)
Topic: Textual Genres & Literature Linguistics

Abstract
Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

Introduction
Authorship Attribution (AA) is the task of identifying authors by their writing style. In addition to being a tool for studying individual language choices, AA is useful for many real-life applications, such as plagiarism detection AUTHOR, multiple accounts detection AUTHOR, and online security AUTHOR. Most work on AA focuses on English, on relatively long texts such as novels and articles AUTHOR where personal style could be mitigated due to editorial interventions. Furthermore, in many real-world applications the texts of disputed authorship tend to be short AUTHOR. The PAN 2020 shared task was originally meant to investigate multilingual AV in fanfiction, focusing on Italian, Spanish, Dutch and English AUTHOR. However, the datasets were eventually restricted to English only, to maximize the amount of available training data AUTHOR, emphasizing the difficulty in compiling large enough datasets for less-resourced languages. AA research in Italian has largely focused on the single case of Elena Ferrante AUTHOR }. The present work seeks a more realistic take, using more diverse, user-generated data namely web forums comments and diary fragments, thereby introducing two novel datasets for this task: ForumFree and Diaries. We cast the AA problem as authorship verification (AV). Rather than identifying the specific author of a text (the most common task in AA), AV aims at determining whether two texts were written by the same author or not AUTHOR. The GLAD system of AUTHOR was specifically developed to solve AV problems, and has been shown to be highly adaptable to new datasets AUTHOR. GLAD uses an SVM with a variety of features including character level ones, which have proved to be most effective for AA tasks AUTHOR, and is freely available. Moreover, AUTHOR show that many of the best models for authorship attribution are based on Support Vector Machines. Hence we adopt GLAD in the present study. More specifically, we run GLAD on our datasets and study the interaction of four different dimensions: topic, gender, amount of evidence per author, and genre. In practice, we design intra-topic, cross-topic, and cross-genre experiments, controlling for gender and amount of evidence per author. The focus on cross-topic and cross-genre AV is in line with the PAN 2015 shared task AUTHOR; this setting has been shown to be more challenging than the task definitions of previous editions AUTHOR. Contributions: We advance AA for Italian introducing two novel datasets, ForumFree and Diaries, which contribute to enhance the amount of available Italian data suitable for AA tasks. Running a battery of experiments on personal writings, we show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

Conclusion
We experimented with AV on Italian forum comments and diary fragments. We compiled two datasets and performed experiments which considered the interaction among topic, gender, length and genre. Even when the texts are short and present more individual variation than traditional texts used in AA, AV is a feasible task, but having more evidence per author improves classification. While making the task more challenging, controlling for gender and topic ensures that the system prioritizes authorship over different data clusters. Although the datasets used are intended for AV problems, they can be easily adapted to other AA tasks. We believe this to be one of the major contributions of our work, as it can help to advance the up-to-now limited AA research in Italian.