An Autonomous Semantic Learning Methodology for Fake News Recognition

Abstract

A persistent challenge to AI theories and technologies is fake news recognition which demands not only syntactic analyses of language expressions, but also their semantics comprehension. This work presents an autonomous system for fake news recognition based on a novel approach of machine semantic learning. A training-free machine learning algorithm of Differential Sentence Semantic Analyses (DSSA) is designed and implemented for fake news detection. A large set of 876 experiments randomly selected from DataCup’ 19 has demonstrated a level of 70.4% accuracy that outperforms the traditional data-driven neural network technologies normally projected at the accuracy level of 55.0%. The DSSA methodology paves a way towards autonomous, training-free, and real-time trustworthy technologies for machine knowledge learning and semantics composition.