lorelupo/dadit_italian_twitter

DADIT

If you are a researcher, contact us (info below) to access the hydrated dataset.

DADIT is a diachronic dataset of Italian Tweets, consisting of 20K Italian Twitter users representative of the Italian Twitter population. The dataset contains User IDs, and their

• 30M Tweets, ranging from 2013 to 2023
  • including their meta-data such as "created at", "likes", "retweets", "is_RT"
• bios
• profile pictures
• location
• other profile information (join_date, n_tweets, following, followers, etc.)

Here, we only share dehydrated Tweet IDs and User IDs. If you are a researcher, contact us (info below) to access the hydrated dataset.

More information can be found in:

• the paper DADIT - A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods.
• the github repo

Contact

If you are a researcher, contact (preferably all of) us to access the full dataset:

• lorenzo + [dot] + lupo2 + [at] + unibocconi + [dot] + it
• paul + [dot] + bose + [at] + unibocconi + [dot] + it
• carlo + [dot] + schwarz + [at] + unibocconi + [dot] + it

Citation

If you use this dataset for your research, please cite:

@inproceedings{lupo-etal-2024-dadit-dataset,
    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.",
}