{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:01:40Z","timestamp":1782316900741,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17060231","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T12:15:43Z","timestamp":1747916143000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1799","authenticated-orcid":false,"given":"Zeinab","family":"Shahbazi","sequence":"first","affiliation":[{"name":"Research Environment of Computer Science (RECS), Kristianstad University, 291 39 Kristianstad, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rezvan","family":"Jalali","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Science, Stockholm University, 106 91 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4093-1156","authenticated-orcid":false,"given":"Zahra","family":"Shahbazi","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering, University of Padova, 35122 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Anno, S., Kimura, Y., and Sugita, S. (2025). Using transformer-based models and social media posts for heat stroke detection. Sci. Rep., 15.","DOI":"10.1038\/s41598-024-84992-y"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1108\/CCIJ-12-2023-0190","article-title":"Climate change as fake news. Positive attribute framing as a tactic against corporate reputation damage from the evaluations of sceptical, right-wing audiences","volume":"30","author":"Chmiel","year":"2025","journal-title":"Corp. Commun. Int. J."},{"key":"ref_3","first-page":"239","article-title":"Misinformation, disinformation, fake news: How do they spread and why do people fall for fake news?","volume":"2","author":"Vasileiadou","year":"2025","journal-title":"Envisioning Future Commun."},{"key":"ref_4","first-page":"171","article-title":"Leveraging Social Media Sentiment Analysis for Enhanced Disaster Management: A Systematic Review and Future Research Agenda","volume":"15","author":"Hashim","year":"2025","journal-title":"J. Syst. Manag. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Podobnikar, T. (2025). Bridging Perceived and Actual Data Quality: Automating the Framework for Governance Reliability. Geosciences, 15.","DOI":"10.3390\/geosciences15040117"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cornale, P., Tizzani, M., Ciulla, F., Kalimeri, K., Omodei, E., Paolotti, D., and Mejova, Y. (2025). The Role of Science in the Climate Change Discussions on Reddit. arXiv.","DOI":"10.1371\/journal.pclm.0000541"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103852","DOI":"10.1016\/j.erss.2024.103852","article-title":"Addressing climate risks through fiscal policy in emerging and developing economies: What do we know and what lies ahead?","volume":"119","year":"2025","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_8","unstructured":"(2008). Software Engineering\u2014Software Product Quality Requirements and Evaluation (SQuaRE)\u2014Data Quality Model (Standard No. ISO\/IEC 25012)."},{"key":"ref_9","unstructured":"Bassolas, A., Massachs, J., Cozzo, E., and Vicens, J. (2024). A cross-platform analysis of polarization and echo chambers in climate change discussions. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9594","DOI":"10.1109\/ACCESS.2024.3353054","article-title":"Echo chambers in online social networks: A systematic literature review","volume":"12","author":"Mahmoudi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., and Byun, Y.C. (2022). NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19127027"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Herasimenka, A., Wang, X., and Schroeder, R. (2025). A Systematic Review of Effective Measures to Resist Manipulative Information About Climate Change on Social Media. Climate, 13.","DOI":"10.3390\/cli13020032"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/17524032.2023.2299756","article-title":"Combatting Climate Change Misinformation: Current Strategies and Future Directions","volume":"18","author":"Chen","year":"2024","journal-title":"Environ. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107769","DOI":"10.1016\/j.chb.2023.107769","article-title":"Correcting climate change misinformation on social media: Reciprocal relationships between correcting others, anger, and environmental activism","volume":"145","author":"Freiling","year":"2023","journal-title":"Comput. Hum. Behav."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/s43247-024-01573-7","article-title":"Hierarchical machine learning models can identify stimuli of climate change misinformation on social media","volume":"5","author":"Rojas","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103822","DOI":"10.1016\/j.csi.2023.103822","article-title":"MRAN: Multimodal relationship-aware attention network for fake news detection","volume":"89","author":"Yang","year":"2024","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"877","DOI":"10.3390\/make6020041","article-title":"Enhancing Legal Sentiment Analysis: A Convolutional Neural Network\u2013Long Short-Term Memory Document-Level Model","volume":"6","author":"Abimbola","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_18","unstructured":"Galaz, V., Metzler, H., Daume, S., Olsson, A., Lindstr\u00f6m, B., and Marklund, A. (2023). AI could create a perfect storm of climate misinformation. arXiv."},{"key":"ref_19","unstructured":"Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., and Zesch, T. (2016, January 16\u201317). QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A., Cocarascu, O., Christodoulopoulos, C., and Mittal, A. (2018, January 1). Teaching Syntax by Adversarial Distraction. Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), Brussels, Belgium.","DOI":"10.18653\/v1\/W18-5501"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hardalov, M., Arora, A., Nakov, P., and Augenstein, I. (2022). A Survey on Stance Detection for Mis- and Disinformation Identification. arXiv.","DOI":"10.18653\/v1\/2022.findings-naacl.94"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_23","first-page":"8354","article-title":"Unveiling implicit deceptive patterns in multi-modal fake news via neuro-symbolic reasoning","volume":"38","author":"Dong","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s13735-023-00296-3","article-title":"A comprehensive survey of multimodal fake news detection techniques: Advances, challenges, and opportunities","volume":"12","author":"Tufchi","year":"2023","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Park, J.S., Camacho, D., Gritzalis, S., and Park, J.J. (2025). Deep Learning Techniques for Enhancing the Efficiency of Security Patch Development. Proceedings of the Advances in Computer Science and Ubiquitous Computing, Springer.","DOI":"10.1007\/978-981-96-5693-6"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., Jalali, R., and Shahbazi, Z. (2025). Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs. Big Data Cogn. Comput., 9.","DOI":"10.3390\/bdcc9050124"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"197290","DOI":"10.1109\/ACCESS.2024.3516883","article-title":"Enhancing Air Quality Forecasting Using Machine Learning Techniques","volume":"12","author":"Shahbazi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Herasimenka, A., Wang, X., and Schroeder, R. (2024). Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media. arXiv.","DOI":"10.3390\/cli13020032"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5753\/jis.2023.3020","article-title":"Fake news detection: A systematic literature review of machine learning algorithms and datasets","volume":"14","author":"Villela","year":"2023","journal-title":"J. Interact. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3700748","article-title":"Modality deep-learning frameworks for fake news detection on social networks: A systematic literature review","volume":"57","author":"Mostafa","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, C., Su, X., Tang, Y., Li, J., and Kassem, M. (2025, May 19). Retrieve-Enhance-Verify: A Novel Approach for Procedural Knowledge Extraction from Construction Contracts via Large Language Models. SSRN 4883720. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4883720.","DOI":"10.2139\/ssrn.4883720"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Popat, K., Mukherjee, S., Yates, A., and Weikum, G. (2018). Declare: Debunking fake news and false claims using evidence-aware deep learning. arXiv.","DOI":"10.18653\/v1\/D18-1003"},{"key":"ref_33","unstructured":"Ruchansky, N., Seo, S., and Liu, Y. (2017, January 6\u201310). CSI: A hybrid deep model for fake news detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, H., Cao, J., Zhang, Y., Guo, J., and Li, J. (2018, January 22\u201326). Rumor detection with hierarchical social attention network. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy.","DOI":"10.1145\/3269206.3271709"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/6\/231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:38:46Z","timestamp":1760031526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/6\/231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["fi17060231"],"URL":"https:\/\/doi.org\/10.3390\/fi17060231","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,22]]}}}