# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 3 Materials and Method
### Misinformation Detection Module  
The Misinformation Detection Module is fundamental in identifying and classifying false or misleading climate change content on social media. This module utilizes cutting-edge Natural Language Processing (NLP) techniques, particularly transformer-based models like BERT, RoBERTa, and GPT variants that are fine-tuned on domain-specific datasets. The first sub-process in this module involves text embedding and semantic understanding, where the model converts input data (e.g., social media posts and comments) into dense vector representations that preserve contextual meaning. These embeddings are crucial for accurately discerning subtle nuances and patterns within the discourse. Following this, the module performs content classification, categorizing each piece of information into defined buckets such as \"Factual\", \"Misleading\", or \"False\". The classification process is supervised by labeled training data and can be continuously improved using active learning approaches. A knowledge graph is integrated to enhance detection accuracy. This graph connects entities (e.g., climate terms, organizations, individuals) with verified relationships, allowing the system to cross-reference claims in the content against trusted scientific and factual databases. This integration helps validate facts and detect logical inconsistencies or misinformation propagation patterns. In addition, the module employs a credibility scoring system that combines several signals, including source reliability (based on historical behavior and trust scores), linguistic cues (e.g., exaggerated language or conspiratorial tone), and contextual coherence with verified knowledge. Based on ongoing content analysis and feedback from downstream modules, the credibility score is dynamic and updated in real time, especially in the Feedback Analysis Layer. A critical component of this module is its feedback-aware retraining mechanism, where flagged errors or borderline cases are looped back into the model's training set for improvement, enabling the system to adapt to emerging misinformation trends. Together, these sub-components form a highly dynamic and responsive misinformation detection pipeline capable of handling large-scale climate \begin{table}
\begin{tabular}{l l l} \hline \hline
**Label** & **Sample Post** & **Annotation Rationale** \\ \hline \multirow{2}{*}{Factual} & “According to NOAA, 2023 was the hottest year & Verified against NOAA climate data; contains \\  & on record globally”. & accurate attribution and context. \\ \hline \multirow{2}{*}{Misleading} & “Wind turbines cause more pollution than coal & Based on partial truth but distorts impact; lacks \\  & when you include manufacturing”. & full life-cycle comparison. \\ \hline \multirow{2}{*}{False} & “CO\({}_{2}\) is not a greenhouse gas, that’s just a myth & Contradicts established climate science; claim \\  & pushed by the UN”. & refuted by IPCC and multiple sources. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample labeled posts and annotation justifications. discourse. This module ensures that only credible, scientifically backed, and contextually accurate content can progress through the analysis pipeline, forming the foundation for insight generation and public awareness strategies. Figure 4 presents a detailed description of the misinformation detection module in the presented system. Algorithm 1 outlines the fine-tuning process of the RoBERTa model for classifying climate misinformation on social media. It begins by initializing a pre-trained RoBERTa model and preparing the labeled dataset through tokenization, padding, and the creation of attention masks. During training, the model iteratively processes mini-batches of data, where it performs forward passes to generate logits, applies the softmax function to compute class probabilities, and calculates the loss using a weighted cross-entropy function. Gradients are backpropagated, and the model parameters are updated using the AdamW optimizer.