Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Source Datasets:
original
Tags:
bias-classification
License:
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dataset_infos.json
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{"byarticle": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.\n- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.\n", "citation": "@article{kiesel2019data,\n title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},\n author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},\n year={2019}\n}\n", "homepage": "https://pan.webis.de/semeval19/semeval19-web/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "hyperpartisan": {"dtype": "bool", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "published_at": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}}}, "supervised_keys": {"input": "text", "output": "label"}, "builder_name": "hyperpartisan_news_detection", "config_name": "byarticle", "version": {"version_str": "1.0.0", "description": "Version Training and validation v1", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2803943, "num_examples": 645, "dataset_name": "hyperpartisan_news_detection"}}, "download_checksums": {"https://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip?download=1": {"num_bytes": 971841, "checksum": "62b4a71275ef2724faddc74d6ff3d782ee9898c732cd66119c7976f6f5168990"}, "https://zenodo.org/record/1489920/files/ground-truth-training-byarticle-20181122.zip?download=1": {"num_bytes": 28511, "checksum": "0c02f4c33317287758e6fbbc976cbfd7a0978923899ddf30cb9dd2cd740af43c"}}, "download_size": 1000352, "post_processing_size": 0, "dataset_size": 2803943, "size_in_bytes": 3804295}, "bypublisher": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.\n- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.\n", "citation": "@article{kiesel2019data,\n title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},\n author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},\n year={2019}\n}\n", "homepage": "https://pan.webis.de/semeval19/semeval19-web/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "hyperpartisan": {"dtype": "bool", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "published_at": {"dtype": "string", "id": null, "_type": "Value"}, "bias": {"num_classes": 5, "names": ["right", "right-center", "least", "left-center", "left"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "validation": {}}}, "supervised_keys": {"input": "text", "output": "label"}, "builder_name": "hyperpartisan_news_detection", "config_name": "bypublisher", "version": {"version_str": "1.0.
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{"byarticle": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.\n- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.\n", "citation": "@article{kiesel2019data,\n title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},\n author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},\n year={2019}\n}\n", "homepage": "https://pan.webis.de/semeval19/semeval19-web/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "hyperpartisan": {"dtype": "bool", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "published_at": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}}}, "supervised_keys": {"input": "text", "output": "label"}, "builder_name": "hyperpartisan_news_detection", "config_name": "byarticle", "version": {"version_str": "1.0.0", "description": "Version Training and validation v1", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2803943, "num_examples": 645, "dataset_name": "hyperpartisan_news_detection"}}, "download_checksums": {"https://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip?download=1": {"num_bytes": 971841, "checksum": "62b4a71275ef2724faddc74d6ff3d782ee9898c732cd66119c7976f6f5168990"}, "https://zenodo.org/record/1489920/files/ground-truth-training-byarticle-20181122.zip?download=1": {"num_bytes": 28511, "checksum": "0c02f4c33317287758e6fbbc976cbfd7a0978923899ddf30cb9dd2cd740af43c"}}, "download_size": 1000352, "post_processing_size": 0, "dataset_size": 2803943, "size_in_bytes": 3804295}, "bypublisher": {"description": "Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.\nGiven a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.\n\nThere are 2 parts:\n- byarticle: Labeled through crowdsourcing on an article basis. 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