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Article-Bias-Prediction/.gitignore +0 -4
Article-Bias-Prediction/LICENSE +0 -201
Article-Bias-Prediction/README.md +0 -38
Article-Bias-Prediction/data/splits/media/test.tsv +0 -1301
Article-Bias-Prediction/data/splits/media/train.tsv +0 -0
Article-Bias-Prediction/data/splits/media/valid.tsv +0 -2357
Article-Bias-Prediction/data/splits/random/test.tsv +0 -1301
Article-Bias-Prediction/data/splits/random/train.tsv +0 -0
Article-Bias-Prediction/data/splits/random/valid.tsv +0 -0

Article-Bias-Prediction/.gitignore DELETED Viewed

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Article-Bias-Prediction/README.md DELETED Viewed

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-# Article-Bias-Prediction
-## Dataset
-The articles crawled from www.allsides.com are available in the ```./data``` folder, along with the different evaluation splits.
-The dataset consists of a total of 37,554 articles. Each article is stored as a ```JSON``` object in the ```./data/jsons``` directory, and contains the following fields:
-1. **ID**: an alphanumeric identifier.
-2. **topic**: the topic being discussed in the article.
-3. **source**: the name of the articles's source *(example: New York Times)*
-4. **source_url**: the URL to the source's homepage *(example: www.nytimes.com)*
-5. **url**: the link to the actual article.
-6. **date**: the publication date of the article.
-7. **authors**: a comma-separated list of the article's authors.
-8. **title**: the article's title.
-9. **content_original**: the original body of the article, as returned by the ```newspaper3k``` Python library.
-10. **content**: the processed and tokenized content, which is used as input to the different models.
-11. **bias_text**: the label of the political bias annotation of the article (left, center, or right).
-12. **bias**: the numeric encoding of the political bias of the article (0, 1, or 2).
-The ```./data/splits``` directory contains the two types of splits, as discussed in the paper: **random** and **media-based**. For each of these types, we provide the train, validation and test files that contains the articles' IDs belonging to each set, along with their numeric bias label.
-## Code
-Under maintenance. To be available soon.
-## Citation
-```
-@inproceedings{baly2020we,
-  author      = {Baly, Ramy and Da San Martino, Giovanni and Glass, James and Nakov, Preslav},
-  title       = {We Can Detect Your Bias: Predicting the Political Ideology of News Articles},
-  booktitle   = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-  series      = {EMNLP~'20},
-  NOmonth     = {November},
-  year        = {2020}
-  pages       = {4982--4991},
-  NOpublisher = {Association for Computational Linguistics}
-}
-```

Article-Bias-Prediction/data/splits/media/test.tsv DELETED Viewed

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