configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': fake
'1': real
splits:
- name: train
num_bytes: 245239522
num_examples: 72134
download_size: 151915950
dataset_size: 245239522
Dataset Card for Dataset Name
Dataset Description
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Dataset Summary
We designed a larger and more generic Word Embedding over Linguistic Features for Fake News Detection (WELFake) dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, we merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training.
Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real).
There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame.
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