manandey/id_clickbait

This is the annotated full version of the dataset.

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.

Data Fields

Annotated

• label_score: label id of the label - 0 for non-clickbait and 1 for clickbait
• title: the title of the news article
• label: the label of the article, either non-clickbait or clickbait

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."
}