# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 4 Results
### Misinformation Detection Performance Evaluation  
This section evaluates the performance of the various AI models used for detecting misinformation presented in Table 6. Among the tested models, a fine-tuned GPT-3 and an ensemble model showed superior accuracy and F1-scores. Transformer-based architectures such as RoBERTa and BERT were also effective, precisely distinguishing between factual and misleading content. Explainability tools like SHAP and LIME were employed to provide interpretability, a key requirement for transparency in misinformation mitigation. The dataset used for misinformation detection was imbalanced, with factual content comprising approximately 72% and misinformation (misleading/false) accounting for 28% of the labeled posts. Consequently, a naive classifier that always predicts the majority class would achieve a baseline accuracy of 72%, which sets the chance-level benchmark. As shown in Table 6, the proposed models significantly outperform this baseline. For instance, the RoBERTa model achieved an accuracy of 90.8% and F1-score of 90.4%, while the ensemble model reached 93.5% accuracy and an F1-score of 93.3%, demonstrating meaningful predictive capability beyond random or majority-class guessing. These improvements are further supported by balanced precision-recall tradeoffs and strong generalization across platforms. The hyperparameter configuration in Table 7 presents the key settings used during the fine-tuning of the RoBERTa model for climate misinformation classification. It includes critical parameters such as the learning rate, batch size, number of training epochs, and \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **P** & **R** & **F1** & **Accuracy** & **Avg. Time** & **Data Source** & **Training** & **Explain** \\  & & & & & & & **Data Size** & **Ability Tool** \\ \hline Majority & 72.0\% & 0.0\% & 0.0\% & 72.0\% & - & - & - & None \\  & & & & & & & & \\ \hline RoBERTa & 91.3\% & 89.6\% & 90.4\% & 90.8\% & 1.3 s & Twitter & 75 k labeled & SHAP \\  & & & & & & & & \\ BERT & 89.7\% & 87.4\% & 88.5\% & 89.2\% & 1.6 s & Reddit & \multirow{2}{*}{80 k samples} & \multirow{2}{*}{LIME} \\  & & & & & & & YouTube & \\ \hline GPT-3 & \multirow{2}{*}{93.1\% (fine tuned)} & \multirow{2}{*}{91.5\%} & \multirow{2}{*}{92.3\%} & \multirow{2}{*}{92.8\%} & \multirow{2}{*}{2.0 s} & \multirow{2}{*}{All sources} & \multirow{2}{*}{100 k samples} & \multirow{2}{*}{OpenAI Tools} \\  & & & & & & & \\ \hline Ensemble & \multirow{2}{*}{94.0\%} & \multirow{2}{*}{92.7\%} & \multirow{2}{*}{93.3\%} & \multirow{2}{*}{93.5\%} & \multirow{2}{*}{2.4 s} & \multirow{2}{*}{All sources} & \multirow{2}{*}{Combined} & \multirow{2}{*}{LIME + SHAP} \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Misinformation detection based on comparative model performance. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Step** & **Time per 10k Record** & **Accuracy** & **Tools** & **Description** \\ \hline Text Normalization & 2.5 s & 99.1\% & spaCy NLTK & Lowercasing, Stemming, \\  & & & & & \\ \hline Language Detection & 1.2 s & 97.8\% & langdetect & Filtered for English French Spanish \\ \hline Relevance Filtering & 3.1 s & 91.3\% & TF-IDF BERT & Matched against climate-related \\  & & & keywords \\ \hline Region Tagging & 2.9 s & 88.7\% & GeoText & Based on IP, hashtags and mentions \\ \hline Spam/Bot Removal & 4.4 s & 93.8\% & Botometer Regex & Detected based on pattern and \\  & & & & & frequency \\ \hline Hashtag Normalization & 2.2 s & 96.1\% & Custom scripts & Standardized for semantic \\  & & & & & alignment \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed preprocessing evaluation. maximum sequence length, which directly influence the model's convergence and generalization performance. Additionally, it specifies the optimizer type (AdamW), dropout rate for regularization, and the loss function employed (weighted cross-entropy) to address class imbalance.