# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 1 Introduction  
Social media platforms have emerged as transformative resource in modern disaster management, enabling government agencies and emergency responders to monitor public needs and sentiment in real time. The 2017 Hurricane Harvey response serves as a clear example of this paradigm shift, where geospatial analysis of social media content facilitated targeted relief operations by identifying critical demands such as emergency evacuations, medical aid requests, and reports of flood severity directly from victims' posts. This crisis highlighted how computational analysis of crowd-sourced data can bridge critical information gaps during large-scale disasters, transforming unstructured public communications into actionable intelligence for prioritizing life-saving interventions [1]. Traditional emergency communication systems frequently collapse under demand surges during disasters, creating critical response delays as overwhelmed call centers fail to process simultaneous assistance requests. This systemic vulnerability has elevated social media as a mission-critical triage platform, enabling affected populations to broadcast geolocated aid requests through mobile devices. Notably, many individuals trapped under debris have utilized mobile devices to request aid through platforms such as Twitter/X. Consequently, essential resources, such as food, medicine, supplies, and donations, were frequently mobilized within a few hours after the onset of disaster events [2]. During the 2023 Turkey-Syria earthquakes, 72% of validated rescue operations originated from crowdsourced intelligence, with entrapped victims using Twitter/X's SMSfallback protocols to transmit precise GPS coordinates. Modern disaster frameworks now operate this digital lifeline through AI-powered triage systems that automatically classify urgent posts (like medical emergencies and supply needs). In past decades, researchers and companies increasingly utilized advanced machine learning techniques to derive meaningful insights from social media data [3]. These techniques enable real-time analysis of trends, sentiments, and social behaviors, providing crucial information for various applications, including disaster response, public health monitoring, and market analysis. By employing machine learning, deep learning, and transformer-based models, researchers can efficiently process vast amounts of social media content, identifying key patterns and informing decision-making processes across multiple domains. For this research, we employed the dataset from HumAID [4] and evaluated multiple state-of-the-art models to identify the most effective approach for text classification. The best-performing model was selected for the classification of social media posts. Additionally, we developed a web-based platform featuring a dynamic interactive map that visualizes the locations of social media users who have enabled location-sharing on their mobile devices. The optimized text classification model was integrated into a web server, where it processes and stores classification results, geolocation data, and the original post content within map markers. The server continuously updates these markers, allowing decision-makers to access user-generated reports, review classified posts, and analyze real-time crisis information. This interactive visualization tool enhances situational awareness, enabling emergency response teams to track unfolding events, assess the severity of incidents based on social media data, and make rapid, informed decisions to improve disaster response efforts. Our key contributions are as follows:  
* We developed and trained a high-performance deep learning model for the accurate classification of diverse disaster-related content from social media data. * We designed and implemented an interactive web-based platform that integrates user geolocation with classified social media posts, supporting emergency responders in monitoring and assessing crisis situations more effectively.