Glazkova, AnnaZakharova, OlgaBasile, ValerioBosco, CristinaGrasso, FrancescaIbrahim, Muhammad OkkySkeppstedt, MariaStede, Manfred2025-02-172025-02-172025-03978-9908-53-114-4https://hdl.handle.net/10062/107176Green 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/Article