# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 4 Results
### Case Study  
Hurricane Harvey was selected as a demonstration case due to the extensive availability of data and its historical significance as one of the costliest and most devastating hurricanes in U.S. history. Making landfall in August 2017, Harvey caused catastrophic flooding, destruction, and widespread displacement, severely impacting communities across southern Texas and parts of Louisiana. During the crisis, social media platforms played an important role in facilitating rescue coordination and enabling crisis communication. As traditional communication infrastructures were overwhelmed or rendered inoperable due to extensive flooding, many stranded individuals turned to social media as their primary means of seeking help. Affected individuals used these platforms to share real-time updates about their locations, conditions, and urgent needs, often providing critical information that traditional emergency channels could not capture promptly. Figure 5 illustrates the word cloud for Hurricane Harvey, highlighting the most frequently occurring terms in social media posts related to the disaster. Key words such as \"shelter\", \"damage\", \"victim\", \"water\", \"donation\", \"help\", and \"relief\" emerge as dominant themes, reflecting critical aspects of disaster response and recovery efforts. Identifying these high-frequency terms is essential for training an accurate text-classification model, as it helps the model recognize and categorize disaster-related information effectively. The response efforts during Hurricane Harvey were notably collaborative, involving a wide range of actors, including federal, state, and local agencies, as well as numerous volunteer organizations. Among the most prominent volunteer groups was the Cajun Navy, a grassroots organization that mobilized quickly to carry out water rescues using privately owned boats [27]. These volunteers played a significant role in reaching areas that were inaccessible to official rescue teams, demonstrating the vital importance of community-led response efforts during large-scale disasters. Hurricane Harvey underscored the necessity of robust disaster preparedness measures, including the implementation of effective early warning systems and reliable crisis communication tools. The disaster highlighted the shortcomings of conventional emergency management infrastructure, particularly in terms of scalability and responsiveness during high-impact events. More importantly, it revealed the potential of social media-based response systems as a complementary tool in disaster management. By leveraging real-time data from social media, emergency response teams were able to enhance situational awareness, allowing for quicker assessments of affected areas and the more accurate identification of individuals in need of urgent assistance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Disaster** & **Class** & **BERT** & **RoBERTa** & **DistilBERT** & **BigBird** & **ALBERT** & **ELECTRA** & **ModernBERT** \\ \hline
2019 Midwestern & 7 & 0.719 & 0.766 & 0.750 & 0.744 & 0.720 & 0.755 & **0.802** \\ U.S. Floods & 7 & 0.674 & 0.689 & 0.680 & 0.678 & 0.674 & 0.677 & **0.698** \\ \hline
2019 Pakistan & 8 & 0.797 & 0.823 & 0.833 & 0.799 & 0.798 & 0.811 & **0.873** \\ Earthquake & & 0.740 & 0.772 & 0.760 & 0.751 & 0.745 & 0.766 & **0.814** \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Hurricane Harvey also brought attention to the need to integrate Artificial Intelligence (AI) and machine learning technologies into disaster management systems. AI-driven analyses of social media data can enable the faster identification of distress signals, categorize needs more effectively, and support decision-making in real time. This technological integration has the potential to significantly improve both the speed and effectiveness of response operations during future disasters. Most major social media platforms provide APIs that enable researchers to access data streams or datasets for various analytical purposes [2].