Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection

Mawqif is the first Arabic dataset that can be used for target-specific stance detection.
This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
We benchmark Mawqif dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89%.

Mawqif Statistics

This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.
The following figure illustrates the labels’ distribution across all targets, and the distribution per target.

Interactive Visualization

To browse an interactive visualization of the Mawqif dataset, please click here

You can click on visualization components to filter the data by target and by class. For example, you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.

Citation

If you feel our paper and resources are useful, please consider citing our work!

@inproceedings{alturayeif-etal-2022-mawqif,
    title = "Mawqif: A Multi-label {A}rabic Dataset for Target-specific Stance Detection",
    author = "Alturayeif, Nora Saleh  and
      Luqman, Hamzah Abdullah  and
      Ahmed, Moataz Aly Kamaleldin",
    booktitle = "Proceedings of the 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://aclanthology.org/2022.wanlp-1.16",
    pages = "174--184",
    abstract = "Social media platforms are becoming inherent parts of people{'}s daily life to express opinions and stances toward topics of varying polarities. Stance detection determines the viewpoint expressed in a text toward a target. While communication on social media (e.g., Twitter) takes place in more than 40 languages, the majority of stance detection research has been focused on English. Although some efforts have recently been made to develop stance detection datasets in other languages, no similar efforts seem to have considered the Arabic language. In this paper, we present Mawqif, the first Arabic dataset for target-specific stance detection, composed of 4,121 tweets annotated with stance, sentiment, and sarcasm polarities. Mawqif, as a multi-label dataset, can provide more opportunities for studying the interaction between different opinion dimensions and evaluating a multi-task model. We provide a detailed description of the dataset, present an analysis of the produced annotation, and evaluate four BERT-based models on it. Our best model achieves a macro-F1 of 78.89{\%}, which shows that there is ample room for improvement on this challenging task. We publicly release our dataset, the annotation guidelines, and the code of the experiments.",
}