{"url":"/dataset/article-bias-prediction","name":"Article Bias Prediction","full_name":null,"description_markdown":"# Article-Bias-Prediction\r\n\r\n## Dataset\r\nThe articles crawled from www.allsides.com are available in the ```./data``` folder, along with the different evaluation splits.\r\n\r\nThe 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:\r\n  1. **ID**: an alphanumeric identifier.\r\n  2. **topic**: the topic being discussed in the article.\r\n  3. **source**: the name of the articles's source *(example: New York Times)*\r\n  4. **source_url**: the URL to the source's homepage *(example: www.nytimes.com)*\r\n  5. **url**: the link to the actual article.\r\n  6. **date**: the publication date of the article.\r\n  7. **authors**: a comma-separated list of the article's authors.\r\n  8. **title**: the article's title.\r\n  9. **content_original**: the original body of the article, as returned by the ```newspaper3k``` Python library.\r\n  10. **content**: the processed and tokenized content, which is used as input to the different models.\r\n  11. **bias_text**: the label of the political bias annotation of the article (left, center, or right).\r\n  12. **bias**: the numeric encoding of the political bias of the article (0, 1, or 2).\r\n\r\nThe ```./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.\r\n\r\n## Code\r\nUnder maintenance. To be available soon.\r\n\r\n## Citation\r\n\r\n```\r\n@inproceedings{baly2020we,\r\n  author      = {Baly, Ramy and Da San Martino, Giovanni and Glass, James and Nakov, Preslav},\r\n  title       = {We Can Detect Your Bias: Predicting the Political Ideology of News Articles},\r\n  booktitle   = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},\r\n  series      = {EMNLP~'20},\r\n  NOmonth     = {November},\r\n  year        = {2020}\r\n  pages       = {4982--4991},\r\n  NOpublisher = {Association for Computational Linguistics}\r\n}\r\n```","description_withheld":null,"homepage":"https://github.com/ramybaly/Article-Bias-Prediction","introduced_date":"2020-10-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/we-can-detect-your-bias-predicting-the","title":"We Can Detect Your Bias: Predicting the Political Ideology of News Articles","first_author":"Ramy Baly","url":null},"license":{"name":"Apache License 2.0","url":"https://github.com/ramybaly/Article-Bias-Prediction/blob/main/LICENSE"},"modalities":[],"tasks":[{"name":"text political leaning classification","url":"/task/text-political-leaning-classification","datasets_with_task":"/datasets/task/text-political-leaning-classification"}],"languages":[],"variants":["Article Bias Prediction"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}