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"""Urdu Fake News Dataset""" |
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from __future__ import absolute_import, division, print_function |
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import glob |
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import os |
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import datasets |
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_CITATION = """ |
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@article{MaazUrdufake2020, |
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author = {Amjad, Maaz and Sidorov, Grigori and Zhila, Alisa and G’{o}mez-Adorno, Helena and Voronkov, Ilia and Gelbukh, Alexander}, |
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title = {Bend the Truth: A Benchmark Dataset for Fake News Detection in Urdu and Its Evaluation}, |
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journal={Journal of Intelligent & Fuzzy Systems}, |
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volume={39}, |
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number={2}, |
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pages={2457-2469}, |
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doi = {10.3233/JIFS-179905}, |
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year={2020}, |
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publisher={IOS Press} |
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} |
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""" |
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_DESCRIPTION = """ |
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Urdu fake news datasets that contain news of 5 different news domains. |
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These domains are Sports, Health, Technology, Entertainment, and Business. |
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The real news are collected by combining manual approaches. |
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""" |
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_URL = "https://github.com/MaazAmjad/Datasets-for-Urdu-news/blob/master/" |
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_URL += "Urdu%20Fake%20News%20Dataset.zip?raw=true" |
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class UrduFakeNews(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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category_list = [ |
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"bus", |
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"hlth", |
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"sp", |
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"tch", |
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"sbz", |
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] |
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def _info(self): |
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labels_list = ["Fake", "Real"] |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"news": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=labels_list), |
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"category": datasets.ClassLabel(names=self.category_list), |
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} |
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), |
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homepage="https://github.com/MaazAmjad/Datasets-for-Urdu-news", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_path = dl_manager.download_and_extract(_URL) |
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input_path = os.path.join(dl_path, "1.Corpus") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"pattern": os.path.join(input_path, "Train", "*", "*.txt")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"pattern": os.path.join(input_path, "Test", "*", "*.txt")}, |
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), |
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] |
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def _generate_examples(self, pattern=None): |
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"""Yields examples.""" |
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for filename in sorted(glob.glob(pattern)): |
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with open(filename, encoding="utf-8") as f: |
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news = "" |
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for line in f: |
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if line == "\n": |
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continue |
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news += line |
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name = os.path.basename(filename) |
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key = name.rstrip(".txt") |
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_class = 1 if ("Real" in filename) else 0 |
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category = "".join([i for i in key if not i.isdigit()]) |
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if category == "": |
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continue |
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category = self.category_list.index(category) |
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yield key, {"news": news, "label": _class, "category": category} |
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