Quanti anni hai? Age Identification for Italian
Authors: Aleksandra Maslennikova, Paolo Labruna, Andrea Cimino, Felice Dell'Orletta (Università di Pisa, CNR-ILC, 2019)
Topic: Textual Genres & Literature Linguistics

Abstract
We present the first work to our knowledge on automatic age identification for Italian texts. For this work we built a dataset consisting of more than 2.400.000 posts extracted from publicly available forums and containing authorship attribution metadata, such as age and gender. We developed an age classifier and performed a set of experiments with the aim of evaluating the possibility of assigning the correct age of an user and which information is useful to tackle this task: lexical or linguistic information spanning across different levels of linguistic descriptions. The performed experiments show the importance of lexical information in age classification, but also that exists writing style that relates to the age of an user.

Introduction
Social media platforms such as Facebook, Twitter and public forums allow users to communicate and share their opinions and to build social relations. The proliferation of such platforms allowed the scientific community to study many communication phenomena such as the analysis of the sentiment AUTHOR or irony AUTHOR. Another related research field is the ”author profiling” one, where the features that allow to discriminate age, gender, or native language of a person are analyzed. These studies are conducted both for forensic and marketing reasons, since the classification of these characteristics allow companies to better focus their marketing campaigns. In the author profiling scenario, many are the studies conducted by the scientific community, that were generally focused on English and Spanish language. The majority of these studies were performed in PAN 1 AUTHOR, a lab at CLEF 2 that holds each year and in which many shared tasks related to the ”authorship attribution” research topic are run. In these shared tasks participants were asked to identify the gender or the age using manually annotated training data from social media platforms. Among the most successful approaches proposed by participants the ones that achieved the best results (AUTHOR, AUTHOR) are based on SVM classifiers exploiting a wide variety of lexical and linguistic features, such as word n–grams, part–of–speech, and syntax. Only recently deep learning based approaches were proposed and have showed very good results especially when dealing with multi–modal data, i.e. text and images posted on Twitter AUTHOR. In the present work we tackle a specific authorship attribution task: the age detection for the Italian language. To our knowledge, this is the first time that such task is performed on Italian. For this reason, we built a multi–topic corpus, developed a classifier which exploits a wide range of linguistic features, and conducted several experiments to evaluate both the newly introduced corpus and the classifier. The main contributions of this work are: i) an automatically built corpus for the age detection task for the Italian language; ii) the development of an age detection system; iii) the study of the impact of linguistic and lexical features.

Conclusion
We presented the first automatically built corpus for the age detection task for the Italian language. By exploiting the publicly available information on the FreeForum platform, we built a corpus consisting of more than 2.400.000 posts and 7.000 different users containing the user’s age information. The first experiments performed through a machine learning based classifier that uses a wide range of linguistic features showed promising results in two different range classification tasks both in the in–domain and out–domain settings. The conducted experiments show that lexicon plays a fundamental role in the age classification task both in in–domain and out–domain scenarios. Lastly, the experiments shown that the corpus, even though if automatically generated, is suitable for real–world applications. We plan to release the full corpus as soon as privacy and legal issues will be fully investigated.