# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 2 Literature Review
### Open-Source Intelligence in Disaster Management  
Social media, particularly Twitter/X, is a valuable tool for gaining situational awareness during disasters by aggregating information from numerous sources in real time [3]. This information can be crucial for crisis responders but may not be presented in a useful format, necessitating platforms such as SMDRM (social media disaster risk management) for streamlining processing [3]. In disaster-prone countries, such as Indonesia, India, or the Philippines, Twitter/X reactions can provide insights into community responses, with sentiment analysis and classification methods aiding in understanding these reactions [12]. During specific disasters, such as earthquakes, Twitter/X discussions can offer vital knowledge for rescue planning, with multimodal analysis helping identify important disaster topics [13]. Moreover, Twitter/X data can be used to monitor disasters and predict hazards using machine-learning techniques that aid in filtering and categorizing related tweets [14]. [PERSON] et al. [15] developed a method to rapidly process and visualize Twitter/X data linked to fire events, therefore reducing the processing time to less than 2 h. [PERSON] et al. [3] created a platform called SMDRM (social media disaster risk management) to streamline the near real-time processing of text and images from Twitter/X during specific events. [PERSON] et al. [12] analyzed Twitter/X sentiments related to disaster management in Indonesia and found approximately 2081 positive and 605 negative sentiments with an accuracy of 84%. [PERSON] et al. [13] investigated Twitter/X discussions related to the 2020 Izmir earthquake by examining the spatiotemporal distribution of earthquake rescue and non-rescue terms. [PERSON] et al. [14] explored the use of Twitter/X in disaster research by focusing on recent machine-learning, deep-learning, and disaster-prediction techniques. In summary, this research has demonstrated the utility of Twitter/X as a real-time source of information during disasters, with applications ranging from situational awareness and community response analysis to hazard prediction and rescue planning. Studies have employed methods such as sentiment analysis, multimodal analysis, and machine-learning techniques to process and extract valuable insights from Twitter/X data, with notable success in reducing the processing time and improving the accuracy. However, there appears to be a gap in the research that focuses on the dynamic evolution of Twitter/X discussions throughout the entire disaster life cycle. Sentence Embeddings, Community Detection-Based Clustering, and Zero-Shot Classification Using Large Language Models  
Sentence embeddings capture the semantic meaning of sentences and are crucial in entity matching by transforming semantic meaning into a high-dimensional vector space [16]. Community detection-based clustering is a fast method for grouping related entities represented by their vector embeddings [16; 17]. In the context of open-source investigations, sentence embeddings, and community detection-based clustering are used to extract and analyze information, with the goal of automating the process and maintaining a high alignment with reality [18]. Zero-shot classification using large language models (LLMs) has been applied across various domains to address the challenges associated with limited annotated datasets. [PERSON] et al. [16] addressed entity matching in record linkage using context-aware sentence embeddings and graph clustering. [PERSON] et al. [17] employed node embeddings and infection subgraphs for real-time rumor containment. [PERSON] et al. [19] found that transformer-based models outperformed classical approaches for dialog data annotation, whereas [PERSON] et al. [20] proposed an unsupervised Graph Clustering Network (GCN)-based community detection method. [PERSON] et al. [21] improved user clustering by integrating social context features into transformer models. In the zero-shot classification domain, [PERSON] et al.