From Data to Grassroots Initiatives: Leveraging Transformer-Based Models for Detecting Green Practices in Social Media

Abstract

Green practices are everyday activities that support a sustainable relationship between people and the environment. Detecting these practices in social media helps track their prevalence and develop recommendations to promote eco-friendly actions. This study compares machine learning methods for identifying mentions of green waste practices as a multi-label text classification task. We focus on transformer-based models, which currently achieve state-of-the-art performance across various text classification tasks. Along with encoder-only models, we evaluate encoder-decoder and decoder-only architectures, including instruction-based large language models. Experiments on the GreenRu dataset, which consists of Russian social media texts, show the prevalence of the mBART encoder-decoder model. The findings of this study contribute to the advancement of natural language processing tools for ecological and environmental research, as well as the broader development of multi-label text classification methods in other domains.