# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 1 Introduction  
Climate change content on social media has faced significant challenges in recent years [1]. One major concern is the ease with which misinformation spreads on these platforms, often without requiring user identity verification. For example, a 2022 study by Climate Action Against Disinformation reported that over 70% of climate misinformation posts analyzed on major platforms remained unflagged. Additionally, the monetization mechanisms on platforms like YouTube and Facebook incentivize the sharing of sensation-alist or misleading information to attract engagement [2, 3]. This flood of diverse content makes it difficult for users to distinguish between credible and unreliable sources. The lack of built-in quality control mechanisms has resulted in widespread information pollution [4]. Moreover, climate change discussions on social media often exacerbate polarization, fostering conflict rather than constructive discourse [5]. These factors make it increasingly difficult for the public to access accurate climate information and participate in informed decision-making. In response, several platforms have begun implementing policies to mitigate climate misinformation. Google, for instance, banned monetization of climate denial content in 2021. TikTok followed in 2023 with climate-specific content moderationguidelines, while Pinterest banned climate misinformation in both ads and user content by 2022 [6]. However, civil society and academic evaluations have revealed that enforcement remains inconsistent and largely ineffective. These gaps highlight the need for a more systematic, AI-based approach to evaluating content quality on social media. In this research [7], data quality is defined based on four key dimensions: (1) accuracy (content correctness), (2) credibility (source reliability), (3) completeness (availability of necessary information), and (4) relevance (alignment with the intended informational need). This approach integrates these dimensions into an automated evaluation system designed to detect, score, and filter climate-related content on social media. To address the identified issues, this study proposes a comprehensive AI-driven framework for evaluating and enhancing the quality of climate-related content shared on social platforms. The proposed system also integrates user feedback analysis and trend forecasting to ensure adaptability to evolving misinformation patterns.  
The main contributions of this research are as follows:  
* **Proposal of a novel multi-stage classification pipeline:** integrating sentiment-aware and domain-specific language models to identify and score the quality of climate-related content across platforms.
* **Development of a content credibility scoring metric:** combining linguistic features, source reliability, and user feedback signals into a unified quality index.
* **Building a cross-platform dataset:** composed of labeled social media posts (Twitter, YouTube, Reddit) with expert-reviewed quality tags, spanning diverse climate-related topics and misinformation themes.
* **Evaluation of system performance:** using metrics such as precision, recall, F1-score, AUC, and Brier score, and benchmarked against state-of-the-art misinformation classifiers.
* **Conduct of ablation studies and bias tests:** to assess robustness across domains, user demographics, and misinformation types, validating generalizability under real-world conditions.