Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability
GESIS – Leibniz Institute for the Social Sciences（GESIS社会科学院研究所）；RWTH Aachen University（亚琛RWTH大学）；University of Graz（格拉茨大学）；Complexity Science Hub（复杂性科学中心）
Fact-checking information from large language models can decrease headline discernment
Observatory on Social Media, Indiana University（社会媒体观察所，印第安纳大学）；FSI Cyber Policy Center, Stanford University（斯坦福大学FSI网络政策中心）；Network Science Institute, Northeastern University（东北大学网络科学研究所）
