# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 2 Literature Review
### Related Works
The datasets included 70,000 disaster-related tweets across seven crises and 10 million COVID-19 pandemic tweets collected between 1 March and 30 April 2020. [PERSON] et al. [35] performed sentiment analysis (SA) to assess post-disaster recovery on the 10 th anniversary of L'Aquila's earthquake using Twitter. Polarity was first defined using a supervised classification based on experts' rules and Grammarly tones and then compared to the outcome of an unsupervised classification using the pre-trained SA machine-learning algorithm developed by MonkeyLearn. The analysis, conducted on 4349 tweets collected from 4 April to 10 April 2019, achieved an overall accuracy of 57%, with a misclassification rate of 43%. The authors suggest that these results can serve as a benchmark for comparing other post-disaster recovery processes using the same Twitter-based sentiment analysis on future anniversaries. [PERSON] et al. [36] conducted a sentiment analysis of tweets manually, categorizing them into eight broader sentiment categories. Orange Data Mining Software was used in an undisclosed version to visualize positive, negative, and neutral sentiments, whereas VOSviewer in an undisclosed version was employed to visualize word frequency in the tweets. This study analyzed 13,156 English tweets posted during the first 27 days of the attack. Performance metrics, such as F1 scores, were not measured because the analysis was performed manually. [PERSON] et al. [37] compared supervised learning approaches for multi-class classification of Twitter data, categorizing tweets into three classes: resource need, resource availability, or others. The system achieved a macro average F1 score of 0.61 and a weighted average F1 score of 0.85. The dataset comprised 2274 tweets, with 194 classified as resource needs, 125 as resource availability, and 1955 as others. The study noted a limitation in that the classification accuracy depended heavily on the type of resource being asked for, as demonstrated by the model's poor performance when trained on earthquake data and tested on COVID-19 data for the availability class. [PERSON] et al. [38] proposed a system using a density-based K-means algorithm for information extraction, ROST Emotion Analysis for sentiment recognition, MS-DICA for opinion classification, and a multi-agent system (MAS) to control false online information during natural disasters. The system achieved a reduction in false information by 2.05% and 3.02%, respectively, using different strategies. This evaluation was conducted using 42,557 samples. [PERSON] et al. [39] developed a framework called Twitter Situational Awareness (TwiSA) that leverages text mining methods, including sentiment analysis and topic modeling, to enhance situational awareness for disaster preparedness, response, and recovery. While the paper does not provide specific F1 scores or accuracy metrics, it demonstrates effectiveness in identifying relevant public concerns during disasters. The system was tested using 217,074 negative tweets. [PERSON] et al. [40] introduced dynamic spatiotemporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas. This approach employs a modified version of the ordering point clustering algorithm to address the heterogeneity of Twitter data and uses Word2 Vec, GloVe, and FastText embeddings to capture both syntactic and semantic similarities. This method was used to monitor the current state of disasters through spatiotemporal and textual analyses of geotagged tweets. The system was evaluated using tweets collected during Hurricane Florence from 12 to 19 September 2018, covering North and South Carolina. The performance, measured using the silhouette coefficient, yielded 0.561 for GloVe, 0.531 for FastText, and 0.516 for Word2 Vec, indicating moderate clustering quality. [PERSON] and [PERSON] [41] explored the best combination of filtering, mapping, and spatial accuracy methods for social media data, particularly in emergency situations.