LIAR Dataset is a benchmark for fake news and political statement classification, containing 12,836 short statements labeled with six fine-grained truthfulness ratings.
What Is the LIAR Dataset?
- Size: 12,836 labeled statements
- Source: PolitiFact fact-checking verdicts
- Labels: Six levels (pants-fire to true)
- Metadata: Speaker, party, context, history
Why LIAR Matters
Fighting misinformation requires nuanced truth assessment—not binary true/false. LIAR provides fine-grained labels and real political context.
LIAR Label Distribution:
pants-fire ████████░░░░░░░░░ (extreme false)
false ██████████████░░░
barely-true███████████░░░░░░
half-true ██████████████░░░
mostly-true████████████░░░░░
true █████████░░░░░░░░
Example Statement:
"Exposed DNC emails show Clinton camp rigged
primary against Bernie Sanders."
Label: half-true (some truth, misleading framing)
Model Performance:
| Model | Accuracy |
|---|---|
| Human (agreement) | ~75% |
| BERT fine-tuned | ~27% (6-class) |
| With metadata | ~41% |
| Binary (true/false) | ~60% |
Six-class classification is very challenging; most work focuses on binary.
liar datasetfake news detectionpolitifact
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