@inproceedings{al-mamari-etal-2024-squad,
    title = "{SQU}ad at {FIGNEWS} 2024 Shared Task: Unmasking Bias in Social Media Through Data Analysis and Annotation",
    author = "Al-Mamari, Asmahan  and
      Al-Farsi, Fatma  and
      Zidjaly, Najma",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of the Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.arabicnlp-1.70/",
    doi = "10.18653/v1/2024.arabicnlp-1.70",
    pages = "646--650",
    abstract = "This paper is a part of the FIGNEWS 2024 Datathon Shared Task and it aims to investigate bias and double standards in media coverage of the Gaza-Israel 2023-2024 conflict through a comprehensive analysis of news articles. The methodology integrated both manual labeling as well as the application of a natural language processing (NLP) tool, which is the Facebook/BART-large-MNLI model. The annotation process involved categorizing the dataset based on identified biases, following a set of guidelines in which categories of bias were defined by the team. The findings revealed that most of the media texts provided for analysis included bias against Palestine, whether it was through the use of biased vocabulary or even tone. It was also found that texts written in Hebrew contained the most bias against Palestine. In addition, when comparing annotations done by AAI-1 and AAI-2, the results turned out to be very similar, which might be mainly due to the clear annotation guidelines set by the annotators themselves. Thus, we recommend the use of clear guidelines to facilitate the process of annotation by future researchers."
}Markdown (Informal)
[SQUad at FIGNEWS 2024 Shared Task: Unmasking Bias in Social Media Through Data Analysis and Annotation](https://preview.aclanthology.org/ingest-emnlp/2024.arabicnlp-1.70/) (Al-Mamari et al., ArabicNLP 2024)
ACL