AI-Based Greenwashing Detection on Digital Platforms Using Multi-Modal Deep Learning

With the growing trend of sustainability comes greenwashing, where companies make green claims to attract green-minded consumers. Environmental claims misuse is on the rise, resulting in greenwashing, where companies misrepresent their products or activities as good for the planet. It is crucial to detect greenwashing to maintain customer trust, particularly in this digital age, where confusion and misleading information are prevalent. This work presents DL approaches to detect greenwashing with a focus on scrutinizing customer skepticism. We recommend the use of DL in the identification of environmental deception claims and can further trace consumer skepticism in digital sources (social media, e-commerce sites, business communications). CNN is built using convolution for hierarchical feature extraction and capturing spatial relations within the input data. GoogleNet refines this with inception modules for multi-scale feature extraction, together with global average pooling to promote feature economy. VGG19, known for its deep structure, is often used in complicated pattern recognition. These models learn from an ESG dataset crafted to embody consumer skepticism, and thus can recognize and discern genuine environmental claims from greenwashing. We have implemented the DL models that outperform the baseline models on the ESG dataset with accuracy (99.67%, 99.56%, 99.24%), Precision (99.47%, 1.00, 98.43%), Recall (99.73%, 98.94%, 99.73%), and F1 -score (99.60%, 99.47%, 99.08%) for GoogleNet, VGG19-Net, and CNN, respectively. The results demonstrate the effectiveness of DL for understanding consumer sentiment against fraudulent marketing tactics on digital platforms, enabling the detection of greenwashing.