Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Indonesian
Size:
10K - 100K
License:
| license: cc-by-4.0 | |
| task_categories: | |
| - text-classification | |
| language: | |
| - id | |
| pretty_name: Indonesian Clickbait Headlines | |
| size_categories: | |
| - 10K<n<100K | |
| ## Dataset Description | |
| - **Homepage:** https://data.mendeley.com/datasets/k42j7x2kpn/1 | |
| - **Repository:** | |
| - **Paper:** [CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!) | |
| - **Dataset URL:** [Dataset](https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip) | |
| - **Point of Contact:** [Andika William](mailto:andika.william@mail.ugm.ac.id), [Yunita Sari](mailto:yunita.sari@ugm.ac.id) | |
| ## 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." | |
| } | |
| ``` |