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

by   Bimal Bhattarai, et al.

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.


page 1

page 2

page 3

page 4


NoFake at CheckThat! 2021: Fake News Detection Using BERT

Much research has been done for debunking and analysing fake news. Many ...

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

Existing fake news detection methods aim to classify a piece of news as ...

Better Reasoning Behind Classification Predictions with BERT for Fake News Detection

Fake news detection has become a major task to solve as there has been a...

DISCO: Comprehensive and Explainable Disinformation Detection

Disinformation refers to false information deliberately spread to influe...

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

In recent years, the proliferation of so-called "fake news" has caused m...

Rating Facts under Coarse-to-fine Regimes

The rise of manipulating fake news as a political weapon has become a gl...

AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs

Machine Learning-as-a-Service systems (MLaaS) have been largely develope...

Please sign up or login with your details

Forgot password? Click here to reset