# 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.

<img width="738" alt="dataStat-2" src="https://user-images.githubusercontent.com/31368075/188299057-54d04e87-802d-4b0e-b7c6-56bdc1078284.png">



# Interactive Visualization

To browse an interactive visualization of the *Mawqif* dataset, please click [here](https://public.tableau.com/views/MawqifDatasetDashboard/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link)
- *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"
}
```
task_categories:text-classification size_categories:1K<n<10K language:ar Stance Detection Sentiment Analysis Sarcasm Detection region:us
{"language": ["ar"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "Mawqif: Stance Detection", "tags": ["Stance Detection", "Sentiment Analysis", "Sarcasm Detection"]}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us 

# 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.

<img width="738" alt="dataStat-2" src="URL



# 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.* 


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

# 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.

<img width="738" alt="dataStat-2" src="URL # 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.* 


If you feel our paper and resources are useful, please consider citing our work!
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us 
 # 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.

<img width="738" alt="dataStat-2" src="URL # 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.* 


If you feel our paper and resources are useful, please consider citing our work!
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us 
# 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.

<img width="738" alt="dataStat-2" src="URL# 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.* 


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