Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
"""Dataset for explainable fake news detection of public health claims.""" | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{kotonya-toni-2020-explainable, | |
title = "Explainable Automated Fact-Checking for Public Health Claims", | |
author = "Kotonya, Neema and Toni, Francesca", | |
booktitle = "Proceedings of the 2020 Conference on Empirical Methods | |
in Natural Language Processing (EMNLP)", | |
month = nov, | |
year = "2020", | |
address = "Online", | |
publisher = "Association for Computational Linguistics", | |
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623", | |
pages = "7740--7754", | |
} | |
""" | |
_DESCRIPTION = """\ | |
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of | |
public health claims. Each instance in the PUBHEALTH dataset has an associated | |
veracity label (true, false, unproven, mixture). Furthermore each instance in the | |
dataset has an explanation text field. The explanation is a justification for which | |
the claim has been assigned a particular veracity label. | |
The dataset was created to explore fact-checking of difficult to verify claims i.e., | |
those which require expertise from outside of the journalistics domain, in this case | |
biomedical and public health expertise. | |
It was also created in response to the lack of fact-checking datasets which provide | |
gold standard natural language explanations for verdicts/labels. | |
NOTE: There are missing labels in the dataset and we have replaced them with -1. | |
""" | |
_DATA_URL = "https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj" | |
_TEST_FILE_NAME = "PUBHEALTH/test.tsv" | |
_TRAIN_FILE_NAME = "PUBHEALTH/train.tsv" | |
_VAL_FILE_NAME = "PUBHEALTH/dev.tsv" | |
class HealthFact(datasets.GeneratorBasedBuilder): | |
"""Dataset for explainable fake news detection of public health claims.""" | |
VERSION = datasets.Version("1.1.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=datasets.Features( | |
{ | |
"claim_id": datasets.Value("string"), | |
"claim": datasets.Value("string"), | |
"date_published": datasets.Value("string"), | |
"explanation": datasets.Value("string"), | |
"fact_checkers": datasets.Value("string"), | |
"main_text": datasets.Value("string"), | |
"sources": datasets.Value("string"), | |
"label": datasets.features.ClassLabel(names=["false", "mixture", "true", "unproven"]), | |
"subjects": datasets.Value("string"), | |
} | |
), | |
supervised_keys=None, | |
homepage="https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_dir = dl_manager.download_and_extract(_DATA_URL) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, _TRAIN_FILE_NAME), | |
"split": datasets.Split.TRAIN, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, _TEST_FILE_NAME), | |
"split": datasets.Split.TEST, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, _VAL_FILE_NAME), | |
"split": datasets.Split.VALIDATION, | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
with open(filepath, encoding="utf-8") as f: | |
label_list = ["false", "mixture", "true", "unproven"] | |
data = csv.reader(f, delimiter="\t") | |
next(data, None) # skip the headers | |
for row_id, row in enumerate(data): | |
row = [x if x != "nan" else "" for x in row] # nan values changed to empty string | |
if split != "test": | |
if len(row) <= 9: | |
elements = ["" for x in range(9 - len(row))] | |
row = row + elements | |
( | |
claim_id, | |
claim, | |
date_published, | |
explanation, | |
fact_checkers, | |
main_text, | |
sources, | |
label, | |
subjects, | |
) = row | |
if label not in label_list: # remove stray labels in dev.tsv, train.tsv | |
label = -1 | |
else: | |
if len(row) <= 10: | |
elements = ["" for x in range(10 - len(row))] | |
row = row + elements | |
( | |
_, | |
claim_id, | |
claim, | |
date_published, | |
explanation, | |
fact_checkers, | |
main_text, | |
sources, | |
label, | |
subjects, | |
) = row | |
if label not in label_list: # remove stray labels in test.tsv | |
label = -1 | |
if label == "": | |
label = -1 | |
yield row_id, { | |
"claim_id": claim_id, | |
"claim": claim, | |
"date_published": date_published, | |
"explanation": explanation, | |
"fact_checkers": fact_checkers, | |
"main_text": main_text, | |
"sources": sources, | |
"label": label, | |
"subjects": subjects, | |
} | |