# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 3 Methods
### Dataset and Data Cleaning  
#### 3.1.1 Dataset  
The dataset HumAID [4] used in this study consists of over 77,000 labeled tweets, sampled from a larger collection of 24 million tweets collected during 19 major real-world disasters that occurred between 2016 and 2019. These disasters include hurricanes, earthquakes, wildfires, and floods, providing a diverse and comprehensive set of disaster-related social media data. The dataset is structured into 11 hierarchically organized classes, capturing various humanitarian and informational aspects of disaster-related content. These categories encompass a wide range of emergency-related themes, such as requests for urgent assistance, reports of infrastructure damage, expressions of sympathy and support, and updates on rescue or relief efforts. This hierarchical structure allows for a more nuanced classification, supporting the development of models capable of effectively distinguishing between different types of disaster-related information for improved crisis response and management. These classes include the following:  
* Caution and advice: Notifications of issued or lifted warnings, along with guidance and safety tips related to the disaster. * Sympathy and support: Posts expressing prayers, thoughts, and emotional support. * Requests or urgent needs: Reports of urgent needs or requests for supplies, including food, water, clothing, money, medical supplies, or blood. * Displaced people and evacuations: Reports of individuals who have relocated due to the crisis, including temporary evacuations. * Injured or dead people: Reports of individuals who have been injured or killed because of the disaster. * Missing or found people: Reports of individuals who are missing or have been found following the disaster event. * Infrastructure and utility damage: Reports of damage to infrastructure, including buildings, houses, roads, bridges, power lines, communication poles or vehicles. * Rescue, volunteering, or donation efforts: Reports of rescue, volunteering, or donation efforts, including transportation to safe locations, evacuations, the distribution of medical aid or food, shelter provisions, and donations of money, goods, or services. * Other relevant information: Tweets that do not fit into any of the above categories but still contain important information relevant to humanitarian aid. * Not humanitarian: Tweets that do not contain information related to humanitarian aid. * Don't know or can't judge: Tweets that are irrelevant or cannot be assessed, including those written in non-English languages. Unlike previous datasets, the HumAID dataset offers a more balanced distribution of disaster types and ensures greater consistency in labeling. This balance enhances the reliability of classification tasks by reducing the impact of class imbalances, which often hinder model performance in disaster-related datasets. Furthermore, HumAID is enriched with tweets that are more likely to originate from areas directly affected by disasters, increasing the presence of firsthand accounts from eyewitnesses and individuals directly impacted by these events. This focus on authentic, location-relevant data makes HumAID particularly valuable for crisis informatics research, as it allows for a more accurate understanding of public sentiment, urgent needs, and situational updates during disaster scenarios. Table 1 presents the distribution of annotations across different disaster events and class labels in HumAID dataset [4], providing an overview of how various categories are represented within the dataset. This distribution is critical for ensuring that the models trained on these data can learn effectively from diverse disaster scenarios and produce accurate classifications across a range of emergency contexts. The dataset applied a filtering process to remove low-prevalence classes, defined as categories containing fewer than 15 tweets.