We invite submissions to the *1st Workshop on Ecology, Environment, and Natural Language Processing <https://econlpws2025.di.unito.it/>*. This workshop will bring together the NLP community and stakeholders from various disciplines to explore how computational linguistics and NLP tools, methods, and applications can help address pressing climate change and environment-related challenges. We are particularly interested in contributions that push the boundaries of linguistics and NLP research in the context of ecological and environmental crises and that foster interdisciplinary collaboration.
The *topics of interests* include, but are not limited to: *Sentiment Analysis of Environmental Topics*: Evaluating public opinions on environmental issues across platforms such as social media, news outlets, and other media (e.g., Bosco et al., 2023 <https://ceur-ws.org/Vol-3596/paper11.pdf>; Ibrohimelmustafaelmustafa et al., 2023 <https://dl.acm.org/doi/pdf/10.1145/3604605>). Automated Linguistic Analysis: Studying grammatical, syntactical and lexical patterns from an ecolinguistic perspective (e.g., Widanti, 2022 <http://influence-journal.com/index.php/influence/article/view/18>), including analyses of corporate environmental reports and other institutional communications (e.g., Gong, 2019 <https://helda.helsinki.fi/server/api/core/bitstreams/5a38650d-71c2-4a62-a338-607ec08ccdc7/content> ). Detection of Anthropocentric and Speciesist Biases Identifying harmful biases in language and NLP applications, and developing methods to mitigate them (e.g., Leach et al., 2021 <https://bpspsychub.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bjso.12561>; Takeshita et al., 2022 <https://www.sciencedirect.com/science/article/pii/S0306457322001558?casa_token=aVOUnzA6S8gAAAAA:tFGhD7PRTlAV5zhJSeuzKl0Fo_R65BIatfHoz5VNU39biM5uwSeKy6cE6ykH8R8mfDLVUfwV> ). *Topic Modeling & Discourse/Frame Analysis*: Investigating how environmental issues are framed in media and political discourse and how these frames influence public perception and policymaking (e.g., Dehler-Holland et al., 2021 <https://www.sciencedirect.com/science/article/pii/S2666389920302336>). *Geo-tagging and sentiment mapping of environmental discussions*: Mapping environmental discussions and sentiments across geographical locations (e.g., Yao & Wang, 2020). *Ecofeminism, environmental justice, and language*: Exploring the intersections of gender, justice, and ecological narratives, and how NLP can help analyze language in these contexts. *Text Classification in Environmental Contexts*: Categorizing texts into specific environmental subfields such as biodiversity, climate change, and conservation, and using NLP to monitor compliance with environmental regulations (e.g., Schimanski et al., 2023 <https://aclanthology.org/2023.emnlp-main.975.pdf>; Grasso & Locci, 2024 <https://link.springer.com/chapter/10.1007/978-3-031-70242-6_29>). Entity Recognition, Relation Extraction, and Environmental Monitoring Identifying and tracking mentions of species, habitats, pollutants, and ecological phenomena in text (e.g., Abdelmageed et al., 2022). Fact-checking & Greenwashing Detection Analyzing corporate sustainability reports for accuracy and detecting greenwashing practices (e.g., Moodaley & Telukdarie, 2023 <https://www.mdpi.com/2071-1050/15/2/1481>; Cojoianu et al., 2020 <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3627157>). Further topics include: - Ecolinguistic applications of NLP. - Large Language Models (LLMs) application in Climata Change and Environmental domain. - Analyzing Social Media for Harmful Environmental Narratives. - Corpora creation and annotation. - Fairness and ethics in environmental data analysis. - Environmental communication in low-resource languages. - Multimodal analysis for ecological and environmental challenges. - Lexical analysis in the context of sustainability and environmental discourse. - Linked Data and Knowledge Graphs on ecological topics. - Language diversity and inclusion in environmental narratives. - Cognitive models and ecological narratives. - NLP for understanding indigenous knowledge in environmental contexts. - Machine learning techniques for analyzing environmental communication. - NLP for tracking environmental legislation and policy discourse. - NLP for analyzing environmental education and awareness campaigns. - Speech recognition technologies to support ecological field research; - Development of educational chatbots or FAQs for raising environmental awareness. Key Dates - *Paper Submission Deadline*: December 16, 2024 - *Notification of Acceptance*: TBA - *Camera-Ready Deadline*: February 3, 2025 - *Workshop Date*: March 2, 2025 *Submission Instructions:* The workshop will accept *archival* submissions, *non-archival* submissions, as well as *research communications* . *Non-archival submissions* refer to new work that will not appear in the proceedings, while *research communications* consist of work already published at other venues (e.g., conferences, journals) that can be presented at the workshop but will not be included in the proceedings. Submissions should follow the *NoDaLiDa/Baltic-HLT 2025 <https://www.nodalida-bhlt2025.eu/call-for-papers> *formatting templates and guidelines; We invite paper submissions of three types: - Regular paper (up to 8 pages) - Short papers (up to 4 pages) - Demo papers (up to 4 pages) For all three submission types, these page limits do not include additional pages with bibliographic references. We do not allow any extra pages for appendices. *Submission and reviewing* will be conducted through *OpenReview* (link to submission TBA) All submissions will undergo *double-blind peer review*, adhering to professional standards.
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