Fact-Check Triage (NLP)

Problem

Fact-checkers can't manually review every public statement, so they need a fast way to triage which claims are most worth investigating first.

Solution

An NLP classifier trained on the real LIAR dataset to triage political statements by likely veracity, with results benchmarked against published literature.

Overview

Fact-checkers can’t manually review every public statement made — the real bottleneck is deciding which claims are worth investigating first.

How it works

An NLP classifier is trained on the real LIAR dataset of labeled political statements, learning linguistic and contextual patterns associated with different levels of truthfulness. The goal isn’t to replace a fact-checker’s judgment — it’s to triage the queue, surfacing the statements most worth a human’s time first. Results are benchmarked against published literature on the same dataset to confirm the approach is competitive, not just plausible-looking.

Result

A transparent, literature-comparable triage signal that helps prioritize scarce fact-checking attention.

Further reading

See Why Most Data Portfolios Get Rejected and Imbalanced Datasets in Machine Learning for more on why benchmarking against a baseline matters.