Author profiling - the computational task of prediction author's demographics from text - has been a popular research topic in the NLP field, and also the focus of a number of prominent shared tasks. Author profiling is a problem of growing importance, with applications in forensics, security, sales and many others. In recent years, text available from social networks has become a primary source for computational models of author profiling, but existing studies are still largely focused on age and gender prediction, and are in many cases limited to the use of English text. Other languages, and other author profiling tasks, remain somewhat less popular. As a means to further this issue, in this work we present initial results of a number of author profiling tasks from a Facebook corpus in the Brazilian Portuguese language. As in previous studies, our own work will focus on both standard gender and age prediction tasks but, in addition to these, we will also address two less usual author profiling tasks, namely, predicting an author's degree of religiosity and IT background status. The tasks are modelled by making use of different knowledge sources, and results of alternative approaches are discussed.
@InProceedings{HSIEH18.354, author = {Fernando Hsieh and Rafael Dias and Ivandré Paraboni}, title = "{Author Profiling from Facebook Corpora}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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