Datasets:
hyperpartisan_news_detection

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	# Lint as: python3
	"""Hyperpartisan News Detection"""


	import os
	import textwrap
	import xml.etree.ElementTree as ET

	import datasets


	_CITATION = """\
	@inproceedings{kiesel-etal-2019-semeval,
	title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
	author = "Kiesel, Johannes and
	Mestre, Maria and
	Shukla, Rishabh and
	Vincent, Emmanuel and
	Adineh, Payam and
	Corney, David and
	Stein, Benno and
	Potthast, Martin",
	booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
	month = jun,
	year = "2019",
	address = "Minneapolis, Minnesota, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/S19-2145",
	doi = "10.18653/v1/S19-2145",
	pages = "829--839",
	abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.",
	}
	"""

	_DESCRIPTION = """\
	Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
	Given 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.

	There are 2 parts:
	- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
	- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.
	"""
	_URL_BASE = "data/"


	class HyperpartisanNewsDetection(datasets.GeneratorBasedBuilder):
	"""Hyperpartisan News Detection Dataset."""

	VERSION = datasets.Version("1.0.1")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="byarticle",
	version=datasets.Version("1.0.0", "Version Training and validation v1"),
	description=textwrap.dedent(
	"""
	This part of the data (filename contains "byarticle") is labeled through crowdsourcing on an article basis.
	The data contains only articles for which a consensus among the crowdsourcing workers existed. It contains
	a total of 645 articles. Of these, 238 (37%) are hyperpartisan and 407 (63%) are not, We will use a similar
	(but balanced!) test set. Again, none of the publishers in this set will occur in the test set.
	"""
	),
	),
	datasets.BuilderConfig(
	name="bypublisher",
	version=datasets.Version("1.0.1", "Version Training and validation v1"),
	description=textwrap.dedent(
	"""
	This part of the data (filename contains "bypublisher") is labeled by the overall bias of the publisher as provided
	by BuzzFeed journalists or MediaBiasFactCheck.com. It contains a total of 750,000 articles, half of which (375,000)
	are hyperpartisan and half of which are not. Half of the articles that are hyperpartisan (187,500) are on the left side
	of the political spectrum, half are on the right side. This data is split into a training set (80%, 600,000 articles) and
	a validation set (20%, 150,000 articles), where no publisher that occurs in the training set also occurs in the validation
	set. Similarly, none of the publishers in those sets will occur in the test set.
	"""
	),
	),
	]

	def _info(self):
	features = {
	"text": datasets.Value("string"),
	"title": datasets.Value("string"),
	"hyperpartisan": datasets.Value("bool"),
	"url": datasets.Value("string"),
	"published_at": datasets.Value("string"),
	}

	if self.config.name == "bypublisher":
	# Bias is only included in the bypublisher config
	features["bias"] = datasets.ClassLabel(names=["right", "right-center", "least", "left-center", "left"])

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=("text", "label"),
	homepage="https://pan.webis.de/semeval19/semeval19-web/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls = {
	datasets.Split.TRAIN: {
	"articles_file": _URL_BASE + "articles-training-" + self.config.name + "-20181122.zip",
	"labels_file": _URL_BASE + "ground-truth-training-" + self.config.name + "-20181122.zip",
	},
	}
	if self.config.name == "bypublisher":
	urls[datasets.Split.VALIDATION] = {
	"articles_file": _URL_BASE + "articles-validation-" + self.config.name + "-20181122.zip",
	"labels_file": _URL_BASE + "ground-truth-validation-" + self.config.name + "-20181122.zip",
	}

	data_dir = {}
	for key in urls:
	data_dir[key] = dl_manager.download_and_extract(urls[key])

	splits = []
	for split in data_dir:
	for key in data_dir[split]:
	data_dir[split][key] = os.path.join(data_dir[split][key], os.listdir(data_dir[split][key])[0])
	splits.append(datasets.SplitGenerator(name=split, gen_kwargs=data_dir[split]))
	return splits

	def _generate_examples(self, articles_file=None, labels_file=None):
	"""Yields examples."""
	labels = {}
	with open(labels_file, "rb") as f_labels:
	tree = ET.parse(f_labels)
	root = tree.getroot()
	for label in root:
	article_id = label.attrib["id"]
	del label.attrib["labeled-by"]
	labels[article_id] = label.attrib

	with open(articles_file, "rb") as f_articles:
	tree = ET.parse(f_articles)
	root = tree.getroot()
	for idx, article in enumerate(root):
	example = {}
	example["title"] = article.attrib["title"]
	example["published_at"] = article.attrib.get("published-at", "")
	example["id"] = article.attrib["id"]
	example = {example, labels[example["id"]]}
	example["hyperpartisan"] = example["hyperpartisan"] == "true"

	example["text"] = ""
	for child in article:
	example["text"] += ET.tostring(child).decode() + "\n"
	example["text"] = example["text"].strip()
	del example["id"]
	yield idx, example