README.md

metadata

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

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

Dataset for "Fact-Aware Fake-news Classification for Indonesian Language"

Data originates from https://saberhoaks.jabarprov.go.id/v2/ ; https://opendata.jabarprov.go.id/id/dataset/ ; https://klinikhoaks.jatimprov.go.id/
The attributes of data are:

1. Label_id: Binary class labels ("HOAX"==1 ; "NON-HOAX"==0).
   
2. Label: Binary class labels ("HOAX" or "NON-HOAX").
   
3. Title: Claim or headline of news article.
   
4. Content: the content of news article.
   
5. Fact: The summary of factual evidence that is either supporting or contradicting the correponding claim.
   
6. References: URL link of news article and the corresponding verdict or factual evidence as the justification of the news article.
   
7. Classification: Fine-grained classification labels for the news article:
   Class labels for saberhoax_data.csv: 'DISINFORMASI', ,'MISINFORMASI', 'FABRICATED CONTENT', 'FALSE CONNECTION', 'FALSE CONTEXT', 'IMPOSTER CONTENT',
   'MANIPULATED CONTENT', 'MISLEADING CONTENT', 'SATIRE OR PARODI', 'BENAR'.
   

Class labels for opendata_jabar.csv: 'BENAR', 'DISINFORMASI (HOAKS)', 'FABRICATED CONTENT', 'FALSE CONNECTION', 'FALSE CONTEXT', 'IMPOSTER CONTENT',
'MANIPULATED CONTENT', 'MISINFORMASI (HOAKS)', 'MISLEADING CONTENT'

Example of usage:

>>> 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'])