README.md

---
license: mit
task_categories:
- text-classification
language:
- id
size_categories:
- 1K<n<10K
---

<b>We do not maintain this repository further. For accessing the most recent Indonesian Fake News dataset that we created, please visit BRIN's dataverse: </b> <url>https://data.brin.go.id/dataset.xhtml?persistentId=hdl:20.500.12690/RIN/7QBRKQ</url></br></br>


Dataset for "Fact-Aware Fake-news Classification for Indonesian Language"</br></br>

Data originates from https://saberhoaks.jabarprov.go.id/v2/ ; https://opendata.jabarprov.go.id/id/dataset/ ; https://klinikhoaks.jatimprov.go.id/ </br>
The attributes of data are: </br>
1. Label_id: Binary class labels ("HOAX"==1 ; "NON-HOAX"==0).</br>
2. Label: Binary class labels ("HOAX" or "NON-HOAX").</br>
3. Title: Claim or headline of news article.</br>
4. Content: the content of news article. </br>
5. Fact: The summary of factual evidence that is either supporting or contradicting the correponding claim.</br>
6. References: URL link of news article and the corresponding verdict or factual evidence as the justification of the news article.</br>
7. Classification: Fine-grained classification labels for the news article:</br>
   Class labels for saberhoax_data.csv: 'DISINFORMASI', ,'MISINFORMASI', 'FABRICATED CONTENT', 'FALSE CONNECTION',
       'FALSE CONTEXT', 'IMPOSTER CONTENT', </br> 'MANIPULATED CONTENT',
       'MISLEADING CONTENT', 'SATIRE OR PARODI', 'BENAR'.</br>
  Class labels for opendata_jabar.csv: 'BENAR', 'DISINFORMASI (HOAKS)', 'FABRICATED CONTENT',
       'FALSE CONNECTION', 'FALSE CONTEXT', 'IMPOSTER CONTENT',</br>
       'MANIPULATED CONTENT', 'MISINFORMASI (HOAKS)',
       'MISLEADING CONTENT' </br>
   </br>

Example of usage:</br>
```python
>>> from datasets import load_dataset
>>> train_dataset = load_dataset(
...     "nlp-brin-id/id-hoax-report",
...     split="train",
...     keep_default_na=False,
... ).select_columns(['Label_id', 'Title', 'Content', 'Fact'])
```