# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 2 Literature Review
### Related Works  
This section summarizes recent related works and evaluates them based on their methodologies, performance metrics, and datasets used. We evaluated each study based on several criteria: the effectiveness of the technology in processing large-scale social media data, the sophistication of the algorithms used for classification or clustering, and the clarity of the reported performance metrics. Additionally, the size of the dataset is considered, as larger datasets generally offer more reliable assessments of a system's performance. [PERSON] et al. [27] developed an approach for event discovery by utilizing unsupervised machine learning and Natural Language Processing (NLP) algorithms to compute events' lifetime and spatial spanning. Their incremental clustering technique employs temporal sliding windows to update the discovered topic clusters with incoming social streams (e.g., tweets). They evaluated their system on a subset of 130,096 tweets and achieved an F1 score of 0.86, with a default number of five clusters. [PERSON] and [PERSON] [28] utilized Natural Language Processing (NLP) techniques, including sentiment analysis, topic modeling, and text classification, to process and analyze social media data during Hurricane Ian. Although specific numerical performance scores, such as accuracy, F1 score, and precision, were not mentioned, the system was applied to 21 million tweets collected between 24 September 2022 and 6 October 2022, during the hurricane. [PERSON] et al. [29] developed a system using Natural Language Processing (NLP) techniques such as Bi-LSTM and Conditional Random Fields (CRF) for extracting structural information from textual emergency plans. The system incorporates scenario matching and semantic relevance matching to generate appropriate emergency plans (EPs) from historical data, specifically for unconventional emergencies. It achieved an F1 score of 0.941 and was tested on 9891 emergency plans. [PERSON] et al. [30] proposed a system for event detection from social media. Their system integrated a semantic keyword generation tool using KeyBERT for dataset preparation. Event detection is performed by CNN and bidirectional LSTM, whereas hierarchical density-based spatial clustering is used for location-inference of events. They conducted experiments over a streamed batch of 4703 tweets, achieving an F1 score of 0.785 in their supervised approach and 0.7 in their unsupervised approach. [PERSON] et al. [31] developed a system integrating Natural Language Processing (NLP), the Analytic Hierarchy Process (AHP), the Entropy Weight Method (EWM), and the Grey TOPSIS method to quickly assess the relief needs of disaster areas and accurately distribute relief materials. The evaluation focused on relief demand urgency rather than specific numerical performance metrics, such as F1 or accuracy. The system was tested on 251,547 posts collected from disaster-affected areas. [PERSON] et al. [32] evaluated a pre-trained sentiment analysis (SA) model developed by the no-code machine-learning platform MonkeyLearn. The model was tested on 695 tweets using hashtags related to the Albanian Earthquake posted between 26 November 2019 and 3 February 2020. The model achieved an accuracy of 63%, indicating that it is acceptable for quick estimation of text polarity during the emergency and early recovery phases after an earthquake. [PERSON] et al. [33] compared several NLP models, including BERT, RoBERTa, DistilBERT, XLNet, and ALBERT, to identify which model performs best in classifying rescue request tweets. The best-performing model was BERT with a CNN classifier, achieving an F1-score of 0.919. The evaluation was conducted on 3191 manually labeled tweets from Hurricane Harvey. [PERSON] et al. [34] employed Latent Dirichlet Allocation (LDA) for topic modeling and BERT embeddings for feature extraction, followed by deep-learning models (ANN, CNN, LSTM) to classify social media data for crisis response. The system achieved F1 scores between 77% and 83.1% across the different deep-learning models.