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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# 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
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