Task-Oriented Automatic Fact-Checking with Frame-Semantics

TL;DR

This paper introduces a new fact-checking approach using frame semantics to better understand claims, supported by a novel dataset and case studies, showing improved evidence retrieval and explainability.

Contribution

It presents a novel frame-semantic based paradigm for fact-checking, along with a new dataset and case studies demonstrating its effectiveness.

Findings

Frame semantics improves evidence retrieval in fact-checking.

The dataset enables large-scale structured claim analysis.

High-impact frames identified for future research.

Abstract

We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future work in this direction.