@inproceedings{di-giovanni-brambilla-2021-content,
    title = "Content-based Stance Classification of Tweets about the 2020 {I}talian Constitutional Referendum",
    author = "Di Giovanni, Marco  and
      Brambilla, Marco",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.socialnlp-1.2",
    doi = "10.18653/v1/2021.socialnlp-1.2",
    pages = "14--23",
    abstract = "On September 2020 a constitutional referendum was held in Italy. In this work we collect a dataset of 1.2M tweets related to this event, with particular interest to the textual content shared, and we design a hashtag-based semi-automatic approach to label them as Supporters or Against the referendum. We use the labelled dataset to train a classifier based on transformers, unsupervisedly pre-trained on Italian corpora. Our model generalizes well on tweets that cannot be labeled by the hashtag-based approach. We check that no length-, lexicon- and sentiment-biases are present to affect the performance of the classifier. Finally, we discuss the discrepancy between the magnitudes of tweets expressing a specific stance, obtained using both the hashtag-based approach and our trained classifier, and the real outcome of the referendum: the referendum was approved by 70{\%} of the voters, while the number of tweets against the referendum is four times greater than the number of tweets supporting it. We conclude that the Italian referendum was an example of event where the minority was very loud on social media, highly influencing the perception of the event. Analyzing only the activity on social media is dangerous and can lead to extremely wrong forecasts.",
}