Predicting E-Commerce Greenwashing Using Random Forest Machine Learning: Legal Analysis and Marketing Management Strategies

Abstract

The rapid growth of e-commerce and the increasing use of environmental claims in product marketing have created challenges in identifying potentially misleading green marketing communication. This study aims to develop a machine learning framework for predicting greenwashing indications in e-commerce products and interpreting the findings from consumer protection and sustainable marketing perspectives. A dataset of 610 products from Shopee and Tokopedia was independently annotated and validated by two annotators. Product descriptions were processed using Natural Language Processing (NLP) and represented using Term Frequency–Inverse Document Frequency (TF-IDF) with unigram and bigram features. Random Forest was employed as the primary classifier, while Support Vector Machine (SVM) and Multinomial Naive Bayes were used as benchmark models. A leakage-safe feature engineering configuration was additionally evaluated. Model performance was assessed using Accuracy, Precision, Recall, F1-score, confusion matrices, and 5-fold stratified cross-validation. On the independent test set, Random Forest with TF-IDF achieved the highest Accuracy (92.62%) and F1-score (95.81%), while 5-fold cross-validation showed that Random Forest with TF-IDF and Strict Feature Engineering achieved the highest mean Accuracy (95.49%) and F1-score (97.42%). Feature importance analysis identified textual structure and specific lexical patterns as predictive signals associated with model decisions. The findings indicate that the proposed framework can support early screening of potentially misleading environmental claims, consumer protection assessment, and more transparent and verifiable green marketing practices. However, model predictions represent predictive signals rather than causal evidence or legal determinations and require further factual and contextual verification

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