@inproceedings{lupo-etal-2024-dadit,
    title = "{DADIT}: A Dataset for Demographic Classification of {I}talian {T}witter Users and a Comparison of Prediction Methods",
    author = "Lupo, Lorenzo  and
      Bose, Paul  and
      Habibi, Mahyar  and
      Hovy, Dirk  and
      Schwarz, Carlo",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.386",
    pages = "4322--4332",
    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.",
}

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.
Lupo, Lorenzo Bose, Paul Habibi, Mahyar Hovy, Dirk Schwarz, Carlo
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods