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
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.