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  ---
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- tags:
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- - sentiment-analysis
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- language:
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  - ind
 
 
 
 
 
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  ---
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- # id_clickbait
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-
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  The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
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-
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  publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
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-
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  Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
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-
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  15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
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-
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  Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
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-
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  sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
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  ## Dataset Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
 
 
 
 
 
 
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  ## Citation
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  ```
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  @article{WILLIAM2020106231,
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  title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
@@ -39,16 +73,14 @@ author = "Andika William and Yunita Sari",
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  keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
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  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."
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  }
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- ```
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-
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- ## License
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-
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- Creative Commons Attribution 4.0 International
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- ## Homepage
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- [https://www.sciencedirect.com/science/article/pii/S2352340920311252#!](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!)
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-
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- ### NusaCatalogue
 
 
 
 
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- For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
 
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+
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  ---
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+ language:
 
 
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  - ind
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+ pretty_name: Id Clickbait
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+ task_categories:
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+ - sentiment-analysis
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+ tags:
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+ - sentiment-analysis
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  ---
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  The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
 
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  publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
 
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  Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
 
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  15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
 
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  Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
 
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  sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
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+
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+ ## Languages
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+
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+ ind
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+
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+ ## Supported Tasks
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+
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+ Sentiment Analysis
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+
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  ## Dataset Usage
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+ ### Using `datasets` library
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+ ```
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+ from datasets import load_dataset
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+ dset = datasets.load_dataset("SEACrowd/id_clickbait", trust_remote_code=True)
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+ ```
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+ ### Using `seacrowd` library
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+ ```import seacrowd as sc
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+ # Load the dataset using the default config
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+ dset = sc.load_dataset("id_clickbait", schema="seacrowd")
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+ # Check all available subsets (config names) of the dataset
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+ print(sc.available_config_names("id_clickbait"))
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+ # Load the dataset using a specific config
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+ dset = sc.load_dataset_by_config_name(config_name="<config_name>")
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+ ```
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+
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+ More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
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+
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+
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+ ## Dataset Homepage
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+
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+ [https://www.sciencedirect.com/science/article/pii/S2352340920311252#!](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!)
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+ ## Dataset Version
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+
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+ Source: 1.0.0. SEACrowd: 2024.06.20.
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+
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+ ## Dataset License
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+
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+ Creative Commons Attribution 4.0 International
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  ## Citation
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+ If you are using the **Id Clickbait** dataloader in your work, please cite the following:
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  ```
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  @article{WILLIAM2020106231,
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  title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
 
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  keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
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  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."
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  }
 
 
 
 
 
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+ @article{lovenia2024seacrowd,
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+ title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
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+ author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
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+ year={2024},
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+ eprint={2406.10118},
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+ journal={arXiv preprint arXiv: 2406.10118}
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+ }
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+ ```