SENADA: A Stacked Ensemble Learning for Native Advertisement Detection in Electronic News

Native advertisements are increasingly common as hybrid commercial content embedded within digital news platforms. Although these ads can enhance user engagement, they often elicit negative reactions due to their concealed commercial intent. A key challenge in detecting native advertisements lies in their lack of explicit labeling, which can mislead readers and compromise the perceived credibility of news content. Prior studies have struggled to accurately identify native ads because of their inability to capture the implicit characteristics embedded in editorial-like text, such as positive sentiment, persuasive language, one-sided perspectives, and references to specific products or companies. To address this issue, this study proposes a deep learning architecture called SENADA (Stacked Ensemble for Native Advertising Analysis). The model integrates BERT-based contextual representations, ensemble learning strategies, and a BiLSTM with an attention mechanism. This design enables the model to effectively identify subtle patterns and contextual cues associated with native advertisements. In addition, we introduce a newly constructed Indonesian-language dataset of news articles, annotated based on the four implicit characteristics of native ads. The dataset was collected from six major e-news portals and manually labeled by trained annotators. Experimental evaluations, based on confusion matrix analysis, demonstrate that SENADA achieves an accuracy of 0.93, along with high values for precision, recall, and F1-score. The proposed model consistently outperforms baseline approaches, particularly in minimizing misclassification of borderline content. These results confirm the model’s effectiveness in detecting native advertisements that exhibit complex and implicit textual patterns. While the model shows promising results, future work is needed to reduce architectural complexity and training time. Further research could explore explainability methods and cross-lingual adaptation to extend the model’s applicability across languages and media contexts.