Explainable Artificial Intelligence for Fake News Detection in Digital Media

Abstract

The speed with which fake news is being spread through digital channels has presented a great challenge, impacting the public opinion, politics, and social cohesion. Several machine learning and deep learning methods have been suggested for detecting fake news, but most of these models are either highly resource-intensive or do not give insights into the news predictions. In response to these challenges, this paper presents an Explainable Artificial Intelligence (XAI) framework for fake news detection that unites the complementary explainability methods: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) with the feature extraction technique, Term Frequency-Inverse Document Frequency (TF-IDF) and the Linear Support Vector Machine (Linear SVM) classifier. The framework is based mainly on the GossipCop and PolitiFact subset of the FakeNewsNet repository. The methodology is divided into four phases, namely: data preprocessing, textual feature extraction using TF-IDF, classification using Linear SVM, and explainability analysis of the classification by using both local and global interpretation techniques. The experimental results confirmed the proposed approach with accuracy of 79.40%, precision of 80.51%, recall of 95.34%, F1-score of 87.30%, and ROC-AUC of 81.40%, and by providing transparent explanations of classification using SHAP and LIME. The achieved outcome shows the proposed framework has a good balance between classification accuracy, simplicity of calculation, and the ease of understanding the model. As a result, the proposed approach is an effective solution for detecting fake news in applications, including automated fact-checking, digital journalism and social media content monitoring.