Social Feature Integration for Entertainment Hoax Detection: Machine Learning and DistilBERT-Fusion

作者：Dodo Zaenal Abidin, Agus Siswanto, Chindra Saputra, Bhetantio Bhetantio · 发表于：Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · 年份：2026 · DOI：10.29207/resti.v10i4.7208

Detecting entertainment hoaxes remains a pressing challenge in the social media era, where short texts, provocative headlines, and rapid dissemination complicate verification. This study compares the effectiveness of classical machine learning models and the transformer DistilBERT, both with and without the integration of social features. The modified GossipCop dataset, consisting of 22,140 entries, includes news titles as the primary text representation and social features such as tweet count, tweet density, and viral indicators. Text was represented using TF-IDF for classical models and DistilBERT tokenization for the transformer, with performance evaluated under stratified 10-fold cross-validation. Results show that incorporating social features consistently improves classical models, with XGBoost + Social achieving the best performance (PR-AUC 0.84; F1-score 0.76), surpassing DistilBERT-Fusion (PR-AUC 0.79). McNemar’s test confirmed significant differences in error distributions, strengthening the reliability of these findings. To reduce reliance on a single empirical dataset, the social feature integration pipeline was additionally validated on the PolitiFact benchmark from a different domain, where incorporating minimal social signals yielded measurable and statistically significant gains over text-only models (Wilcoxon p = 0.0137), confirming the robustness of the social feature effect beyond the entertainment domain. Overall, the results highlight that for short-text ...