An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political News

2026-08-29

AI Summary

本文提出了一种时间一致性评分(TCS)方法，通过提取时间事实、构建知识图谱、验证一致性和生成解释来检测政治新闻中的时间不一致问题。

Abstract

Temporal inconsistencies, such as mandates attributed outside their real interval, events presented as past before they occurred, or inverted causal sequences, are a form of political disinformation that evades style-based fake news detectors: a well-written article with a single wrong date carries no lexical signal of falsehood. This paper introduces the Temporal Coherence Score (TCS), a continuous, intrinsically interpretable metric that quantifies the temporal coherence of a news article, computed by a four-stage pipeline: extraction of temporal facts, construction of a temporal knowledge graph, hierarchical verification against internal consistency rules and external reference sources, and score aggregation with automatically generated explanations. Verification combines eight internal checkers derived from Allen's interval algebra with a five-level external hierarchy ranging from a locally stored reference knowledge base of 1{,}256 curated political facts to live Wikidata SPARQL queries. On a benchmark of 100 political news articles with injected temporal errors, the system reaches a precision of 0.909 at the selected operating threshold, with a single residual false positive, a profile deliberately tuned for human-in-the-loop fact-checking assistance, where false alarms are costlier than missed detections. Unlike lexical baselines that output only a binary label, every flagged article is accompanied by the inconsistency type, the entities involved, and the reference source that contradicts the claim.

Problem

• temporal inconsistencies
• political disinformation
• fake news detectors

Innovation

• Temporal Coherence Score
• Explainable AI
• Political Disinformation Detection
• Temporal Knowledge Graph