Article Bias Prediction

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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} } ``