annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: Indonesian Clickbait Headlines
dataset_info:
- config_name: annotated
features:
- name: id
dtype: string
- name: title
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-clickbait
'1': clickbait
splits:
- name: train
num_bytes: 1268698
num_examples: 15000
download_size: 150769127
dataset_size: 1268698
- config_name: raw
features:
- name: id
dtype: string
- name: title
dtype: string
- name: source
dtype: string
- name: date
dtype: string
- name: category
dtype: string
- name: sub-category
dtype: string
- name: content
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 81669386
num_examples: 38655
download_size: 150769127
dataset_size: 81669386
Dataset Card for Indonesian Clickbait Headlines
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines
- Repository: CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines
- Paper: CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines
- Leaderboard:
- Point of Contact: Andika William, Yunita Sari
Dataset Summary
The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
Indonesian
Dataset Structure
Data Instances
An example of the annotated article:
{
'id': '100',
'label': 1,
'title': "SAH! Ini Daftar Nama Menteri Kabinet Jokowi - Ma'ruf Amin"
}
>
Data Fields
Annotated
id
: id of the sampletitle
: the title of the news articlelabel
: the label of the article, either non-clickbait or clickbait
Raw
id
: id of the sampletitle
: the title of the news articlesource
: the name of the publisher/newspaperdate
: datecategory
: the category of the articlesub-category
: the sub category of the articlecontent
: the content of the articleurl
: the url of the article
Data Splits
The dataset contains train set.
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
Creative Commons Attribution 4.0 International license
Citation Information
@article{WILLIAM2020106231,
title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
journal = "Data in Brief",
volume = "32",
pages = "106231",
year = "2020",
issn = "2352-3409",
doi = "https://doi.org/10.1016/j.dib.2020.106231",
url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252",
author = "Andika William and Yunita Sari",
keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
}
Contributions
Thanks to @cahya-wirawan for adding this dataset.