vaccine_opinion_it

<strong>Classifier of opinion conveyed by vaccine-related content in Italian language</strong></br> A monolingual model for classifying the opinion conveyed through vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts, Twitter tweets, Instagram media and YouTube videos. It is a fine-tuned version of bert-base-multilingual-cased.

<strong>Model output</strong></br> The model classifies each input into one of three distinct classes:</br>

<ul> <li>Anti-vax</li> <li>Neutral</li> <li>Pro-vax</li> </ul>

<strong>Citation info and BibTeX entries</strong></br>

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316258" target="_blank">Dynamics and triggers of misinformation on vaccines</a>

@article{Bru2025,
  title={Dynamics and triggers of misinformation on vaccines},
  author={Brugnoli, Emanuele and Delmastro, Marco},
  journal={PLOS ONE},
  year={2025},
  volume={20},
  number={1},
  pages={e0316258},
  doi={10.1371/journal.pone.0316258}
}

<a href="https://academic.oup.com/pnasnexus/article/3/11/pgae474/7831539" target="_blank">Unveiling the Hidden Agenda: Biases in News Reporting and Consumption</a>

@article{,
  title={Unveiling the Hidden Agenda: Biases in News Reporting and Consumption},
  author={Galeazzi, Alessandro and Peruzzi, Antonio and Brugnoli, Emanuele and Delmastro, Marco and Zollo, Fabiana},
  journal={PNAS NEXUS},
  year={2024},
  volume={3},
  number={11},
  pages={pgae474},
  doi={10.1093/pnasnexus/pgae474}
}