---
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

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### 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.

### Supported Tasks and Leaderboards

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### Languages

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## Dataset Structure

### Data Instances

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### Data Fields

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### Data Splits

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## Dataset Creation

### Curation Rationale

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### Source Data

#### Initial Data Collection and Normalization

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### Annotations

#### Annotation process

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### Personal and Sensitive Information

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## Considerations for Using the Data

### Social Impact of Dataset

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### Discussion of Biases

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### Other Known Limitations

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## Additional Information

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### Contributions

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