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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-language:
-- bg
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- fact-checking
-paperswithcode_id: null
-pretty_name: Clickbait/Fake News in Bulgarian
-dataset_info:
-  features:
-  - name: fake_news_score
-    dtype:
-      class_label:
-        names:
-          0: legitimate
-          1: fake
-  - name: click_bait_score
-    dtype:
-      class_label:
-        names:
-          0: normal
-          1: clickbait
-  - name: content_title
-    dtype: string
-  - name: content_url
-    dtype: string
-  - name: content_published_time
-    dtype: string
-  - name: content
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 24480402
-    num_examples: 2815
-  - name: validation
-    num_bytes: 6752242
-    num_examples: 761
-  download_size: 8569575
-  dataset_size: 31232644
----
-
-# Dataset Card for Clickbait/Fake News in Bulgarian
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news)
-- **Repository:** [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data)
-- **Paper:** [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf)
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned. 
-The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks.
-
-The dataset was prepared for the Hack the
-Fake News hackathon. It was provided by the
-[Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is
-available in [Gitlab](https://gitlab.com/datasciencesociety/). 
-
-The corpus was automatically collected, and then annotated by students of journalism. 
-
-The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
-and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples. 
-
-There is 98% correlation between fake news and clickbaits.
-
-One important aspect about the training dataset is that it contains many repetitions.
-This should not be surprising as it attempts to represent a natural distribution of factual
-vs. fake news on-line over a period of time. As publishers of fake news often have a group of
-websites that feature the same deceiving content, we should expect some repetition.
-In particular, the training dataset contains
-434 unique articles with duplicates. These articles have three reposts each on average, with
-the most reposted article appearing 45 times.
-If we take into account the labels of the reposted articles, we can see that if an article
-is reposted, it is more likely to be fake news.
-The number of fake news that have a duplicate in the training dataset are 1018 whereas,
-the number of articles with genuine content
-that have a duplicate article in the training set is 322.
-
-(The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).)
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-Bulgarian
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-Each entry in the dataset consists of the following elements: 
-
-* `fake_news_score` - a label indicating whether the article is fake or not
-
-* `click_bait_score` - another label indicating whether it is a click-bait
-
-* `content_title` - article heading
-
-* `content_url` - URL of the original article
-
-* `content_published_time` - date of publication
-
-* `content` - article content 
-
-
-### Data Splits
-
-The **training dataset** contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
-and 1,968 (i.e., 70%) are click-baits; 
-
-The **validation dataset** contains 761 testing examples. 
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@tsvm](https://github.com/tsvm), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

clickbait_news_bg.py

@@ -1,119 +0,0 @@
-# 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 with clickbait and fake news in Bulgarian. """
-
-
-import openpyxl  # noqa: requires this pandas optional dependency for reading xlsx files
-import pandas as pd
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{clickbait_news_bg,
-title = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},
-authors={Data Science Society},
-year={2017},
-url={https://gitlab.com/datasciencesociety/case_fake_news/}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://gitlab.com/datasciencesociety/case_fake_news/"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLs = {
-    "default_train": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Training_Set.xlsx",
-    "default_validation": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Validation_Set.xlsx",
-}
-
-
-class ClickbaitNewsBG(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.1.0")
-    DEFAULT_CONFIG_NAME = "default"
-
-    def _info(self):
-        if self.config.name == "default":
-            features = datasets.Features(
-                {
-                    "fake_news_score": datasets.features.ClassLabel(names=["legitimate", "fake"]),
-                    "click_bait_score": datasets.features.ClassLabel(names=["normal", "clickbait"]),
-                    "content_title": datasets.Value("string"),
-                    "content_url": datasets.Value("string"),
-                    "content_published_time": datasets.Value("string"),
-                    "content": datasets.Value("string"),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download(_URLs)
-
-        return [
-            datasets.SplitGenerator(
-                name=spl_enum,
-                gen_kwargs={
-                    "filepath": data_dir[f"{self.config.name}_{spl}"],
-                    "split": spl,
-                },
-            )
-            for spl, spl_enum in [
-                ("train", datasets.Split.TRAIN),
-                ("validation", datasets.Split.VALIDATION),
-            ]
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        keys = [
-            "fake_news_score",
-            "click_bait_score",
-            "content_title",
-            "content_url",
-            "content_published_time",
-            "content",
-        ]
-        with open(filepath, "rb") as f:
-            data = pd.read_excel(f, engine="openpyxl")
-            for id_, row in enumerate(data.itertuples()):
-                row_dict = dict()
-                for key, value in zip(keys, row[1:]):
-                    if key == "fake_news_score":
-                        row_dict[key] = "legitimate" if value == 1 else "fake"
-                    elif key == "click_bait_score":
-                        row_dict[key] = "normal" if value == 1 else "clickbait"
-                    else:
-                        row_dict[key] = str(value)
-                yield id_, row_dict

dataset_infos.json

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-{"default": {"description": "Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.\n", "citation": "@InProceedings{clickbait_news_bg,\ntitle = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},\nauthors={Data Science Society},\nyear={2017},\nurl={https://gitlab.com/datasciencesociety/case_fake_news/}\n}\n", "homepage": "https://gitlab.com/datasciencesociety/case_fake_news/", "license": "", "features": {"fake_news_score": {"num_classes": 2, "names": ["legitimate", "fake"], "names_file": null, "id": null, "_type": "ClassLabel"}, "click_bait_score": {"num_classes": 2, "names": ["normal", "clickbait"], "names_file": null, "id": null, "_type": "ClassLabel"}, "content_title": {"dtype": "string", "id": null, "_type": "Value"}, "content_url": {"dtype": "string", "id": null, "_type": "Value"}, "content_published_time": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "clickbait_news_bg", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 24480402, "num_examples": 2815, "dataset_name": "clickbait_news_bg"}, "validation": {"name": "validation", "num_bytes": 6752242, "num_examples": 761, "dataset_name": "clickbait_news_bg"}}, "download_checksums": {"https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Training_Set.xlsx": {"num_bytes": 6543801, "checksum": "ffb10237c03f06f73b65a63e3eb507ab8683d5bb71b35233b3f7703ff3a60c7e"}, "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Validation_Set.xlsx": {"num_bytes": 2025774, "checksum": "56207dfa58f9b3eb20444a919e739321768cd37ad7e53612658a80f483eb003a"}}, "download_size": 8569575, "post_processing_size": null, "dataset_size": 31232644, "size_in_bytes": 39802219}}

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