DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

Lorenzo Lupo, Paul Bose, Mahyar Habibi, Dirk Hovy, Carlo Schwarz

Abstract
Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don’t leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.
Anthology ID:
2024.lrec-main.386
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
4322–4332
Language:
URL:
https://aclanthology.org/2024.lrec-main.386
DOI:
Bibkey:
Cite (ACL):
Lorenzo Lupo, Paul Bose, Mahyar Habibi, Dirk Hovy, and Carlo Schwarz. 2024. DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4322–4332, Torino, Italia. ELRA and ICCL.
Cite (Informal):
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods (Lupo et al., LREC-COLING 2024)
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