# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 2 Related Works
### Text Classification Models  
Various research approaches have leveraged both traditional machine learning and transformer-based techniques to classify different types of social media text streams, particularly in disaster response and crisis informatics. Early approaches to this problem predominantly relied on supervised machine learning methods, where incoming social media texts were classified into two or more predefined categories. These models were trained on labeled datasets to recognize patterns in disaster-related content, enabling them to categorize new, unseen posts based on their textual and contextual features. In an early study, [PERSON] et al. [12] introduced a dataset specifically designed for sentiment analysis and evaluated the performance of eight machine learning models. These traditional models demonstrated varying levels of effectiveness, setting a foundation for future research in disaster-related sentiment classification. As the field evolved, researchers began incorporating deep learning methods to improve text classification performance, particularly for handling complex patterns in large-scale social media datasets. [PERSON] et al. [13] reported that both machine learning and deep learning models can produce highly competitive outcomes under specific conditions, highlighting the importance of selecting the appropriate method based on dataset characteristics and application requirements. Further advancements were made by [PERSON] et al. [14], who proposed using Convolutional Neural Networks (CNNs) for identifying valuable information from tweets during crisis situations. CNNs, known for their success in image processing tasks, were adapted for text analyses by leveraging their ability to detect local patterns and relationships within the data. In addition, [PERSON] et al. [15] established a benchmark for classifying disaster-related social media images using deep learning models, demonstrating the potential of multimodal approaches that combine both textual and visual data for more comprehensive disaster response systems. In recent years, Transformer-based models have emerged as the dominant architecture in the field of natural language processing, offering significant improvements over previous methods. Transformers, particularly those based on BERT and its variants, have shown remarkable success in understanding contextual relationships in text. For instance, [PERSON] et al. [4] constructed a dataset specifically for disaster-related text classification and conducted comparative analyses between traditional machine learning algorithms and Transformer-based methods, including BERT and its derivatives. [PERSON] et al. [3] introduced an innovative methodology that harnesses the capabilities of prompt-based Large Language Models (LLMs) to enhance disaster response efforts. The authors found that GPT-3.5 Turbo consistently outperformed alternative models in terms of informativeness, clarity, and attribution. Gemini and Command also demonstrated a strong performance, exhibiting a comparable overall report quality and coherence. In contrast, Llama showed relatively weaker results, particularly regarding clarity and detail. The results highlightedthe superior performance of Transformer models, particularly in capturing complex linguistic patterns and handling the imbalanced class distributions commonly found in disaster-related datasets. However, deep learning models often demonstrate suboptimal performance in the domain of crisis informatics, and existing models face several limitations that hinder the development of more advanced and effective deep learning solutions. Key challenges include data scarcity, which restricts the availability of large, high-quality datasets necessary for training robust models. Additionally, class imbalance remains a significant issue, as certain disaster-related categories, such as urgent rescue needs or medical emergencies, are often underrepresented in the data. This imbalance can lead to biased models that fail to recognize rare but critical situations.