@inproceedings{dinu-etal-2025-antisemro,
    title = "{A}nti{S}em{RO}: Studying the {R}omanian Expression of Antisemitism",
    author = "Dinu, Anca  and
      Moldovan, Andreea C.  and
      Marincea, Adina",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.36/",
    pages = "291--298",
    abstract = "This study introduces an annotated dataset for the study of antisemitic hate speech and attitudes towards Jewish people in Romanian, collected from social media. We performed two types of annotation: with three simple tags ({`}Neutral', `Positive', `Negative'), and with five more refined tags (Neutral', `Ambiguous', `Jewish Community', Solidarity', `Zionism', `Antisemitism'). We perform several experiments on this dataset: clusterization, automatic classification, using classical machine learning models and transformer-based models, and sentiment analysis. The three classes clusterization produced well grouped clusters, while, as expected, the five classes clusterization produced moderately overlapping groups, except for `Antisemitism', which is well away from the other four groups. We obtained a good F1-Score of 0.78 in the three classes classification task with Romanian BERT model and a moderate F1-score of 0.62 for the five classes classification task with a SVM model. The lowest negative sentiment was contained in the `Neuter' class, while the highest was in `Zionism', and not in `Antisemitism', as expected. Also, the same `Zionism' category displays the highest level of positive sentiment."
}