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            <namePart type="given">Valerio</namePart>
            <namePart type="family">Basile</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T From Data to Grassroots Initiatives: Leveraging Transformer-Based Models for Detecting Green Practices in Social Media
%A Glazkova, Anna
%A Zakharova, Olga
%Y Basile, Valerio
%Y Bosco, Cristina
%Y Grasso, Francesca
%Y Ibrohim, Muhammad Okky
%Y Skeppstedt, Maria
%Y Stede, Manfred
%S Proceedings of the 1st Workshop on Ecology, Environment, and Natural Language Processing (NLP4Ecology2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-114-4
%F glazkova-zakharova-2025-data
%X 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.
%U https://aclanthology.org/2025.nlp4ecology-1.2/
%P 1-9

Markdown (Informal)

[From Data to Grassroots Initiatives: Leveraging Transformer-Based Models for Detecting Green Practices in Social Media](https://aclanthology.org/2025.nlp4ecology-1.2/) (Glazkova & Zakharova, NLP4Ecology 2025)

• From Data to Grassroots Initiatives: Leveraging Transformer-Based Models for Detecting Green Practices in Social Media (Glazkova & Zakharova, NLP4Ecology 2025)

ACL

• Anna Glazkova and Olga Zakharova. 2025. From Data to Grassroots Initiatives: Leveraging Transformer-Based Models for Detecting Green Practices in Social Media. In Proceedings of the 1st Workshop on Ecology, Environment, and Natural Language Processing (NLP4Ecology2025), pages 1–9, Tallinn, Estonia. University of Tartu Library.