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Transformer vs. Classical ML for Fake News Detection: A SHAP-Explainable Comparative Study on WELFake

by Moqbel Tawhib Salah Abdo, Hosen Md Roki
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 119
Year of Publication: 2026
Authors: Moqbel Tawhib Salah Abdo, Hosen Md Roki
10.5120/ijca464629276ffa

Moqbel Tawhib Salah Abdo, Hosen Md Roki . Transformer vs. Classical ML for Fake News Detection: A SHAP-Explainable Comparative Study on WELFake. International Journal of Computer Applications. 187, 119 ( Jun 2026), 63-99. DOI=10.5120/ijca464629276ffa

@article{ 10.5120/ijca464629276ffa,
author = { Moqbel Tawhib Salah Abdo, Hosen Md Roki },
title = { Transformer vs. Classical ML for Fake News Detection: A SHAP-Explainable Comparative Study on WELFake },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2026 },
volume = { 187 },
number = { 119 },
month = { Jun },
year = { 2026 },
issn = { 0975-8887 },
pages = { 63-99 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number119/explainable-fake-news-detection-using-bert-and-shap-analysis/ },
doi = { 10.5120/ijca464629276ffa },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-01T03:10:02.860094+05:30
%A Moqbel Tawhib Salah Abdo
%A Hosen Md Roki
%T Transformer vs. Classical ML for Fake News Detection: A SHAP-Explainable Comparative Study on WELFake
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 119
%P 63-99
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automated fake news detection requires both high predictive accuracy and interpretable outputs for responsible deployment. This study benchmarks seven models — Logistic Regression, Linear SVM, Random Forest, XGBoost, BERT, RoBERTa, and DistilBERT — under identical experimental conditions on the WELFake corpus (72,134 articles). RoBERTa achieves the best performance (accuracy=96.84%, macro F1=0.968, AUC-ROC=0.9943, MCC=0.937), confirmed via five-fold stratified cross-validation (96.52%±0.27%) and McNemar’s pairwise significance tests (χ²>6.63, p<0.01). A SHAP explainability framework identifies sensationalist framing, anonymous sourcing, and absence of quantitative specificity as the dominant linguistic markers of fabricated content. An ablation study demonstrates an 8.65 pp gain from combining title and body text. Together, these results establish that predictive accuracy and interpretability are complementary objectives in responsible fake news AI.

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Index Terms

Computer Science
Information Sciences

Keywords

Fake News Detection BERT RoBERTa DistilBERT XGBoost Explainable AI (XAI) SHAP NLP WELFake Transformer Models Misinformation Statistical Significance McNemar's Test

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