<![CDATA[The rapid spread of disinformation through digital platforms constitutes a serious threat to social cohesion and public health. Debunking—the systematic refutation of false information using verified evidence—has emerged as a key countermeasure, yet manual identification and classification of debunking strategies is labor-intensive and difficult to scale. This study addresses this gap through a mixed-methods design integrating qualitative corpus analysis with automated machine learning (ML) classification. A corpus of 120 debunking articles published by three leading Indonesian fact-checking institutions (Kominfo AIS, Mafindo, and Cek Fakta Kompas, 2022–2024) was first manually annotated by two trained coders (Cohen's κ = 0.82) to identify four dominant debunking strategies: (1) contextual correction with emotional narrative framing; (2) source authority endorsement; (3) visual verification and reverse image search; and (4) myth-versus-fact inoculation format. This annotated corpus was subsequently used as a training dataset to develop and benchmark five NLP-based text classification models: TF-IDF + Support Vector Machine (SVM), TF-IDF + Random Forest, IndoBERT fine-tuned, IndoBERT with data augmentation (IndoBERT-Aug), and XGBoost with linguistic features. The IndoBERT-Aug model achieved the highest overall performance (macro-averaged F1 = 0.847, Precision = 0.851, Recall = 0.843), substantially outperforming the SVM baseline (F1 = 0.612). Logistic regression analysis further identified three significant moderators of debunking effectiveness: correction timeliness within 6 hours (OR=2.80, p<0.01), content readability (OR=0.68, p<0.01), and multi-platform distribution (OR=1.84, p<0.05), with the full model explaining 41% of variance (Nagelkerke R²=0.41). These contributions are formalized into the Indonesian Debunking Effectiveness Model (IDEM), a framework integrating automated strategy detection with evidence-based deployment guidelines for scalable counter-disinformation operations.]]>
