{"type":"ScholarlyArticle","title":"Enhancing Online Climate Discourse: A Two-Stage Framework for Climate Content Categorization and Moderation","authors":[{"name":"Apoorva Upadhyaya","slug":"apoorva-upadhyaya","given":"Apoorva","family":"Upadhyaya","affiliation":"Research Center , Leibniz University Hannover"},{"name":"Wolfgang Nejdl","slug":"wolfgang-nejdl","given":"Wolfgang","family":"Nejdl","affiliation":"Research Center , Leibniz University Hannover"},{"name":"Marco Fisichella","slug":"marco-fisichella","given":"Marco","family":"Fisichella","affiliation":"Research Center , Leibniz University Hannover"}],"abstract":"Climate change is one of the most pressing global challenges that requires urgent adaptation and resilience efforts, highlighting the need for both scientific solutions and effective communication. In the digital age, online content plays a key role in shaping climate narratives. Therefore, previous research has mainly focused on public perception or categorized content by topics such as impacts, mitigation, policy, etc. Despite these efforts, identifying discussions that address climate change adaptation is crucial for monitoring resilience and assessing public sentiment, while recognizing denial narratives helps combat misinformation. Moreover, the public's exposure to online climate content can either lead to or hinder climate action, emphasizing the need for climate content moderation. To address these issues, we propose a novel multi-stage framework where stage 1 categorizes climate-related content into adaptation, resilience, and denial while stage 2 moderates content by enhancing or intervening based on its alignment with climate goals. We present a novel dataset by manually annotating publicly available tweets and news articles into different climate categories with the help of a taxonomy developed by domain experts. Extensive experiments with benchmark climate and other domain datasets validate the efficacy of our prediction stage, while human and external evaluations confirm the relevance of our moderation stage.","doi":"10.24963/ijcai.2025/1099","url":"https://ijcai.org/proceedings/2025/1099","pdf":"https://ijcai.org/proceedings/2025/1099.pdf","markdown":"https://ijcai.org/proceedings/2025/1099.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1099","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI and Social Good","track":"AI and Social Good","number":1099,"pages":{"first":9890,"last":9898},"keywords":["Data Mining: General","Multidisciplinary Topics and Applications: General","Natural Language Processing: General","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Check, 2024] Google Fact Check. 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