ClaimBuster is an automated system developed at the University of Texas at Arlington that identifies check-worthy factual claims in text — the first and crucial step in the automated fact-checking pipeline. It scores sentences based on their likelihood of containing important, verifiable factual claims.

How ClaimBuster Works

• Input: Takes text input — a debate transcript, speech, news article, or any text containing potential claims.
• Scoring: Each sentence receives a check-worthiness score from 0 to 1, indicating how likely it is to contain a factual claim that is worth verifying.
• Ranking: Sentences are ranked by their scores, allowing fact-checkers to focus on the most important claims first.
• Classification: Sentences are classified into categories — Non-Factual Sentence (NFS), Unimportant Factual Sentence (UFS), and Check-Worthy Factual Sentence (CFS).

Technology

• Training Data: Trained on thousands of sentences from US presidential debates, political speeches, and other public discourse, labeled by professional fact-checkers.
• Features: Uses linguistic features (named entities, numbers, sentiment), structural features (sentence position, length), and contextual features (topic, speaker).
• Models: Evolved from SVM classifiers to transformer-based models (BERT fine-tuning) for better performance.

Applications

• Live Debate Monitoring: Process debate transcripts in real-time to highlight check-worthy claims as they are made.
• News Analysis: Scan news articles to identify factual claims that should be verified.
• Social Media Monitoring: Flag viral posts containing check-worthy claims for fact-checker review.
• Fact-Checker Workflow: Prioritize which claims to check first based on check-worthiness scores.

API and Access

• ClaimBuster API: Publicly available API that scores text for check-worthiness.
• Integration: Can be integrated into newsroom workflows, social media monitoring tools, and fact-checking platforms.

Significance

ClaimBuster addresses a fundamental bottleneck in fact-checking — there are far more claims made than fact-checkers can verify. By automatically identifying the most important claims, it helps fact-checkers allocate their limited time to the claims that matter most.

ClaimBuster represents an important step toward scalable fact-checking — it doesn't verify claims itself but ensures that human fact-checkers focus on what matters.