AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media

Zeinab Shahbazi (), Rezvan Jalali and Zahra Shahbazi
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Zeinab Shahbazi: Research Environment of Computer Science (RECS), Kristianstad University, 291 39 Kristianstad, Sweden
Rezvan Jalali: Department of Computer and Systems Science, Stockholm University, 106 91 Stockholm, Sweden
Zahra Shahbazi: Department of Environmental Engineering, University of Padova, 35122 Padova, Italy

Future Internet, 2025, vol. 17, issue 6, 1-21

Abstract: In the digital age, climate change content on social media is frequently distorted by misinformation, driven by unrestricted content sharing and monetization incentives. This paper proposes a novel AI-based framework to evaluate the data quality of climate-related discourse across platforms like Twitter and YouTube. Data quality is defined using key dimensions of credibility, accuracy, relevance, and sentiment polarity, and a pipeline is developed using transformer-based NLP models, sentiment classifiers, and misinformation detection algorithms. The system processes user-generated content to detect sentiment drift, engagement patterns, and trustworthiness scores. Datasets were collected from three major platforms, encompassing over 1 million posts between 2018 and 2024. Evaluation metrics such as precision, recall, F1-score, and AUC were used to assess model performance. Results demonstrate a 9.2% improvement in misinformation filtering and 11.4% enhancement in content credibility detection compared to baseline models. These findings provide actionable insights for researchers, media outlets, and policymakers aiming to improve climate communication and reduce content-driven polarization on social platforms.

Keywords: climate change; data quality; sustainability; social media (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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