Skip to content
Log in
Open access

Explainable Artificial Intelligence for Fake News Detection in Digital Media

Ilyass Mzili Otmane Houdaif Zakaria Benlalia
Jul 2026 · EDRAAK · 0 citations

TL;DR

An Explainable Artificial Intelligence (XAI) framework for fake news detection that unites the complementary explainability methods: SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations with the feature extraction technique, Term Frequency-Inverse Document Frequency (TF-IDF) and the Linear Support Vector Machine (Linear SVM) classifier.

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.

DOI Semantic Scholar
Read PDF

Similar papers

Jul 2026

AI-Based Fake News Detection Using Natural Language Processing

This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing techniques and multiple machine learning algorithms to automatically classify news articles as real or fake.

Shahid Khan, Abdul Majid Farooqi · 0 citations
Open access Aug 2026

Detecting the Deception : An Intelligent Machine Fake News Detection

The current landscape of digital and social media has made any news universally available. Yet, today's media is marred by dissemination of fabricated news. Known as fake news, it erodes the trust of society, leads to social disorder, and hinders social order. This paper presents a machine learning methodology to detect fake news. Natural language processing (NLP) is a branch of computer science that aids in detection of fictitious news. In this case, NLP is used to pre-process news articles. This includes, but is not limited to, text completing, tokenization, the elimination of stop words, and feature extraction by means of term frequency and inverse document frequency (TF-IDF). News articles are then classified as real or fake by various machine learning classification algorithms. These include, but are not limited to, logistic regression, Naïve Bayes, random forest, and support vector machines. A variety of metrics are used to determine the efficacy of each algorithm. These include accuracy, precision, recall, and the F1-score. The machine learning and NLP methods presented in this paper prove that they have the capability to identify misleading news, and this work provides a starting point for machine learning and NLP methods in fictitious news detection. It also describes the hurdles in reliable fictitious news detection and identifies areas for future research in improving reliable and effective fictitious news detection frameworks.

R. B, Sree Dhanya C, Udayakumar C · 0 citations
Open access Aug 2026

Fake News Detection Using Machine Learning and LLM Embeddings: A Comparative Study of TF-IDF and BERT Representations on the Welfake Dataset

The proposed framework highlights the potential of integrating transformer-based language models with classical machine learning algorithms to build robust and scalable fake news detection systems.

Umme Noor Us Saqa, Sreenivasa B. R. · 0 citations
Conference Jul 2026

Hybrid Explainable Fake News Detection Using CNNs and Frequent Pattern Mining

For smart societies and digital communication ecosystems, the wide spread of fake news on online platforms is a big challenge. This highlights the need for reliable and explainable detection methods. Deep learning approaches often achieve strong predictive performance; however, they usually work as black boxes and lack explainability. In this paper, we propose a hybrid approach for fake news detection that integrates a Convolutional Neural Network (CNN) text classifier with frequent pattern mining to generate human-understandable explanations. The proposed CNN model performs a binary classification of news content into real or fake news, while the pattern mining approach allows identifying discriminative frequent n-gram patterns for each class and links them to the label predicted by the CNN model to justify predictions. Experimental evaluations were conducted on a fake news dataset and the results show that the proposed approach scores high prediction performance while providing clear and evidence-based explanations.

Mahmoud Skafi, Julie Bu Daher · 0 citations
Open access Jul 2026

Detection of Fake News on Social Media Using Network Science Approach

This paper proposes modeling data through ego networks, extracting features from the underlying network, matching with textual features, and using traditional machine learning algorithms to detect and identify fake news on social networks.

Jhonathan Carvalho, B. Travençolo, F. Pereira · 0 citations
Open access Jul 2026

Fake News Identification Using Hybrid Transformer Ensemble Approach

A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.

M. E. Babu, G. Sukanya · 0 citations