# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 3 Materials and Method
### Overview of Methodological Framework and Key Hyperparameters
The system then assigns a credibility score to each piece of content based on its factual consistency, source reliability, and historical trustworthiness. Training involved the use of weighted loss functions to address class imbalance, with label smoothing (\(\sigma\) = 0.1) to reduce overfitting on hard false positives. Dropout (\(p\) = 0.3) and data augmentation techniques like synonym replacement and sentence shuffling are implemented. **Step 4: Feedback Analysis Layer** To ensure dynamic adaptability, the system incorporates a Feedback Analysis Layer that monitors user interactions, including sentiment analysis of comments and the frequency of reports or flags. Trust metrics and emotional responses are quantified using NLP sentiment scoring techniques. Feedback is looped back into the system through an active learning framework to iteratively refine the misinformation models based on user behavior and emerging discourse patterns. To minimize bias in feedback interpretation, demographic signal anonymization and fair weighting adjustments using the fairness-aware reweighting method are included. **Step 5: Trend and Impact Analysis** The Trend and Impact Analysis module identifies misinformation narratives gaining traction and maps their origin. It uses topic modeling algorithms such as LDA and BERTopic to visualize the evolution of discourse. Time-series forecasting techniques like Prophet and LSTM are employed to predict future trends and understand the potential trajectory of misinformation spread. Temporal correlation of misinformation bursts is measured using dynamic time warping (DTW) and Granger causality tests. **Step 6: Insight Generation and Reporting** Insights derived from prior modules are consolidated in the Insight Generation phase. This stage automatically generates summary reports for researchers, media professionals, and policymakers. It highlights emerging risks and misinformation surges and offers strategic recommendations for mitigation. Moreover, it provides platforms with data-driven content suggestions to promote credible discourse. The system generates stakeholder-specific dashboards using Plotly and Dash, providing transparency in misinformation trends, source clusters, and evolving topics. These outputs can be exported in JSON, CSV, or PDF formats. **Step 7: Evaluation and Refinement** Finally, the Evaluation and Refinement module ensures the system remains accurate and relevant. This involves continuous performance evaluation using precision, recall, F1-score, and feedback alignment metrics. System performance is periodically reviewed through error analysis, and training datasets are updated with help from human reviewers to maintain model robustness and adaptability. This process performs ablation studies to assess the impact of feedback loops and external knowledge graphs. Fairness metrics, such as disparate impact and equal opportunity difference, are calculated to monitor bias. Error analysis is conducted using LIME and SHAP to visualize misclassification regions.