Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

TL;DR

This paper introduces an interpretable Tsetlin Machine-based framework for fake news detection that outperforms some baselines in accuracy and provides explainability through logic-based representations and credibility scores.

Contribution

It presents a novel, interpretable fake news detection framework using Tsetlin Machines, capturing lexical and semantic features with improved transparency.

Findings

Significantly outperforms baselines by at least 5% accuracy

Achieves higher F1-score than BERT and XLNet

Provides meaningful explanations through word and negation decomposition

Abstract

The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use the clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5\%$ in terms of accuracy, with the added benefit of an interpretable logic-based representation. Further, our approach provides higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model's explainability, demonstrating how it decomposes into meaningful words and their negations.