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.

我们构建、验证和公开发布了包括用户数据、文本和图像的具有代表性的DADIT数据集，用于研究社交媒体用户的性别和年龄预测模型，并发现采用基于XLM的分类器结合推文作为特征可以显著提高性别和年龄的预测效果。

DADIT：意大利Twitter用户人口统计数据集及预测方法比较