# AI-Driven Framework for Evaluating Climate Misinformation and Data Quality on Social Media
## 3 Materials and Method
### Overview of Methodological Framework and Key Hyperparameters  
This study uses a modular deep-learning framework implemented in PyTorch (version 2.1.0, developed by Meta AI, Menlo Park, CA, USA), with transformer models (e.g., BERT and RoBERTa) fine-tuned using the AdamW optimizer (learning rate = 2 \(\times\) 10\({}^{-5}\), batch size = 16, max epochs = 10). Training and evaluation were conducted on NVIDIA V100 GPUs. Model selection and early stopping were based on the validation F1-score. Cross-validation was applied using a 5-fold stratified split to ensure balanced representation of misinformation categories. The results were compared against baseline classifiers, including logistic regression, SVMs, and vanilla RNNs. the metrics used for evaluation include precision, recall, F1-score, calibration error, and macro-average AUC. **Step 1: Data Collection Module** The process begins with the Data Collection Module, where public posts, comments, and interactions related to climate change are gathered from various social media platforms via APIs. The platforms include Twitter/X (via Academic Research API), Reddit (Pushshirt API), YouTube (Data API v3), Facebook (CrowdTangle API), and TikTok (web-scraping). The data were collected between January 2023 and February 2025 using a keyword list curated from IPCC, UNFCCC, and ClimateAction.org. The sampling criteria included a minimum engagement threshold (\(\geq\)10 likes or shares) to ensure discourse relevance. In cases where API access is limited, web-scraping techniques are employed to supplement data acquisition. Alongside textual data, associated metadata, such as timestamps, user engagement metrics (likes, shares, comments), and hashtags, are stored to preserve contextual relevance. A total of 97,542 posts were constructed, with a subset of 10,000 manually labeled for misinformation classification using guidelines from the IFCN (International Fact-Checking Network), and cross-referenced with known false claims from PolitiFact, ClimateFeedback.org, and Snopes. The dataset is split 70/15/15 for training, validation, and testing. **Step 2: Data Preprocessing Engine** Next, the collected raw data undergo cleaning and normalization in the Data Preprocessing Engine. This stage includes converting text to lowercase, removing stopwords, and applying stemming or lemmatization. The data are then filtered based on language, geographical origin, and relevance to climate discourse. Spam content, advertisements, and bot-generated posts are also identified and eliminated using rule-based filters and machine-learning heuristics. Tokenization is performed using HuggingFace's BERT tokenizer, with a max sequence length of 256 tokens. Custom feature  
Figure 1: Overall architecture of the proposed data quality evaluation on social media. engineering includes the Hashtag decomposition (e.g., #ClimateScam \(\rightarrow\) \"climate scam\"), TF-IDF-based keyword filtering to exclude off-topic content, and bot detection using Botometer v4.0 and removal of automated accounts. Named Entity Recognition (NER) using spaCy (version 3.7.2, developed by Explosion AI, Berlin, Germany) for tagging organizations and climate-related entities is applied. **Step 3: Misinformation Detection Module** Following preprocessing, content enters the Misinformation Detection Module, where advanced transformer-based NLP models such as BERT, RoBERTa, and GPT are utilized for content classification. These models are fine-tuned on labeled datasets to distinguish between factual, misleading, and false information. Multiple pretrained models are subject to experimental testing include BERTbase, RoBERTa-large, and DeBERTa-v3. The final model is a fine-tuned RoBERTa-large that achieved the highest macro F1-score (0.84). For multilingual inputs (e.g., from Tik-Tok or YouTube), XLM-RoBERTa is used. Additionally, knowledge graphs and external fact-checking databases are integrated to verify claims and enhance detection accuracy.