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@@ -20,16 +20,16 @@ dataset_info:
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  dtype: string
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  splits:
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  - config_name: factuality_prediction
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  features:
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  - name: file
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  splits:
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  - name: test
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  - config_name: original
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  features:
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  - name: file
@@ -105,4 +105,93 @@ configs:
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  data_files:
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  - split: train
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  path: original/train-*
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  dtype: string
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  splits:
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  - name: train
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+ num_bytes: 163041
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  num_examples: 738
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  - name: full_train
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+ num_bytes: 951010
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  num_examples: 4403
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  - name: test
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+ num_bytes: 384327
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  num_examples: 1788
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  download_size: 718605
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+ dataset_size: 1498378
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  - config_name: factuality_prediction
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  features:
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  - name: file
 
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  dtype: string
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  splits:
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  - name: train
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+ num_bytes: 606722
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  num_examples: 2826
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  - name: full_train
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+ num_bytes: 944929
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  num_examples: 4403
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  - name: test
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+ num_bytes: 381863
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  num_examples: 1788
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  download_size: 927856
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+ dataset_size: 1933514
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  - config_name: original
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  features:
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  - name: file
 
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  data_files:
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  - split: train
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  path: original/train-*
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+ license: unknown
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+ task_categories:
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+ - text-classification
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+ language:
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+ - pt
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+ - por
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+ pretty_name: FactNews
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+ size_categories:
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+ - 1K<n<10K
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+ multilinguality:
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+ - monolingual
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+ language_creators:
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+ - found
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+ annotations_creators:
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+ - expert-generated
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+ tags:
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+ - subjectivity
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+ - mediabias
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+ - media-bias
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  ---
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+
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+ ## Disclaimer
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+
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+ *I am not the author of this dataset, this is a modified version of the FactCheck dataset on HuggingFace, the original data is made avaliable by Vargas et. al, 2023 and can be downloaded from the link: https://github.com/franciellevargas/FactNews*
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+
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+ *Modifications:*
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+ - *The "original" subset contains the unmodified original CSV*
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+ - *The subsets for the task of "bias_prediction" and "factuality_prediction" were splited between train (70%) AND test (30%) by randomly selecting
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+ sentences grouped by their id_article. This configuration difers from the authors, who made a 90%/10% 10-fold split on the papers.*
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+ - *Each task contains an unbalanced split (full-train) and the balanced-split (train)*
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+
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+ # Sentence-Level Annotated Dataset for Predicting Factuality of News and Bias of Media Outlets in Portuguese
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+
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+ Automated fact-checking and news credibility verification at scale require accurate prediction of news factuality and media bias.
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+ Here, we introduce a large sentence-level dataset, titled FactNews, composed of 6,191 sentences expertly annotated according to factuality
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+ and media bias definitions proposed by AllSides. We used the FactNews to assess the overall reliability of news sources by formulating two
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+ text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate
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+ that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence,
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+ the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of the
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+ entire media outlet. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese,
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+ both dataset and baseline were proposed for Brazilian Portuguese. The following table describes in detail the FactNews labels, documents, and stories:
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+
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+ | Factual| Quotes | Biased | Total sentences | Total news stories | Total news documents |
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+ | :--- | :---: | ---: | ---: | ---: | ---: |
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+ | 4,242 | 1,391 | 558 | 6,161 | 100 | 300 |
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+
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+ ### Sources:
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+ - Media 1: Folha de São Paulo
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+ - Media 2: Estadão
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+ - Media 3: O Globo
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+
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+ ### Paper Results:
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+
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+ Sentence-Level Media Bias Prediction (90%/10% 10-fold split)
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+ - 67% (F1-Score) by Fine-tuned mBert-case
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+
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+ Sentence-Level Factuality Prediction (90%/10% 10-fold split)
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+ - 88% (F1-Score) by Fine-tuned mBert-case
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+
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+
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+ ## Citation
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+
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+ ```
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+ Vargas, F., Jaidka, K., Pardo, T.A.S., Benevenuto, F. (2023). Predicting Sentence-Level Factuality of News and Bias of Media Outlets. Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pp.1197--1206. Varna, Bulgaria. Association for Computational Linguistics (ACL).
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+
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+ ```
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+
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+ **Bibtex**
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+ ```
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+ @inproceedings{vargas-etal-2023-predicting,
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+ title = "Predicting Sentence-Level Factuality of News and Bias of Media Outlets",
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+ author = "Vargas, Francielle and
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+ Jaidka, Kokil and
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+ Pardo, Thiago and
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+ Benevenuto, Fabr{\'\i}cio",
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+ editor = "Mitkov, Ruslan and
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+ Angelova, Galia",
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+ booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
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+ month = sep,
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+ year = "2023",
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+ address = "Varna, Bulgaria",
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+ publisher = "INCOMA Ltd., Shoumen, Bulgaria",
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+ url = "https://aclanthology.org/2023.ranlp-1.127",
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+ pages = "1197--1206",
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+ }
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+ ```
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+ ## Dataset Description
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+
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+ - **Homepage:** https://github.com/franciellevargas/FactNews
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+ - **Paper:** [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://aclanthology.org/2023.ranlp-1.127)