LingML: Linguistic-Informed Machine Learning for Enhanced Fake News Detection

TL;DR

LingML integrates linguistic features into machine learning models to significantly improve fake news detection accuracy, interpretability, and generalizability, especially during the pandemic, demonstrating promising results with fewer errors.

Contribution

This paper introduces LingML, a novel approach combining linguistics and ML for fake news detection, enhancing performance and explainability over existing methods.

Findings

Error rate reduced to below 2 out of 10 attempts using linguistic input alone.

Outperforms existing models with a 1.8% lower average error rate when combined with large-scale NLP models.

Provides highly explainable knowledge, aiding interpretability and trust in fake news detection.

Abstract

Nowadays, Information spreads at an unprecedented pace in social media and discerning truth from misinformation and fake news has become an acute societal challenge. Machine learning (ML) models have been employed to identify fake news but are far from perfect with challenging problems like limited accuracy, interpretability, and generalizability. In this paper, we enhance ML-based solutions with linguistics input and we propose LingML, linguistic-informed ML, for fake news detection. We conducted an experimental study with a popular dataset on fake news during the pandemic. The experiment results show that our proposed solution is highly effective. There are fewer than two errors out of every ten attempts with only linguistic input used in ML and the knowledge is highly explainable. When linguistics input is integrated with advanced large-scale ML models for natural language processing, our solution outperforms existing ones with 1.8% average error rate. LingML creates a new path with linguistics to push the frontier of effective and efficient fake news detection. It also sheds light on real-world multi-disciplinary applications requiring both ML and domain expertise to achieve optimal performance.