@inproceedings{mohtaj-moller-2022-tub,
    title = "{TUB} at {WANLP}22 Shared Task: Using Semantic Similarity for Propaganda Detection in {A}rabic",
    author = {Mohtaj, Salar  and
      M{\"o}ller, Sebastian},
    editor = "Bouamor, Houda  and
      Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Rambow, Owen  and
      Bougares, Fethi  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Khalifa, Salam  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.wanlp-1.57/",
    doi = "10.18653/v1/2022.wanlp-1.57",
    pages = "501--505",
    abstract = "Propaganda and the spreading of fake news through social media have become a serious problem in recent years. In this paper we present our approach for the shared task on propaganda detection in Arabic in which the goal is to identify propaganda techniques in the Arabic social media text. We propose a semantic similarity detection model to compare text in the test set with the sentences in the train set to find the most similar instances. The label of the target text is obtained from the most similar texts in the train set. The proposed model obtained the micro F1 score of 0.494 on the text data set."
}