# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 2 Related Works
### Existing Dataset  
Numerous datasets have been developed to support sentiment analysis and crisis-related classification tasks during natural disasters. These resources have significantly contributed to the NLP community, facilitating advancements in research and development within the crisis informatics domain. By providing labeled datasets, benchmark models, and evaluation metrics, they have enabled researchers to improve disaster-related text classification, sentiment analysis, and real-time information retrieval, ultimately enhancing emergency response and decision-making systems. 1. SWDM2013 [5] includes two distinct collections: the Joplin set, which contains 4400 labeled tweets from the May 2011 tornado that struck Joplin, Missouri, and the Sandy set, featuring 2000 tweets from Hurricane Sandy in October 2012. These datasets provide valuable insights into the social media discourse during two significant disaster events and serve as foundational data for early research in disaster-related sentiment analysis and classification. 2. Disaster Response Data resource [4] contains 30,000 tweets categorized into 36 different classes, collected from various disasters, including the 2010 earthquakes in Haiti and Chile, the 2010 floods in Pakistan, and the 2012 occurrence of Hurricane Sandy in the United States. This dataset is particularly valuable for training models across diverse disaster contexts, enabling the creation of more generalized crisis classification systems. 3. CrisisNLP [6] is another comprehensive dataset featuring 50,000 human-annotated tweets collected from 19 disaster events between 2013 and 2015. These tweets are categorized using multiple labeling schemes, with classes designed for humanitarian disaster response and health-related emergencies. The diversity of annotations allows researchers to explore various aspects of disaster response, including needs identification, resource allocation, and medical emergencies. 4. CrisisMMD [7] adopts a multimodal and multitask approach, consisting of 18,000 labeled tweets accompanied by associated images. This dataset supports both text- and image-based classification, enabling researchers to develop models that analyze not only textual information but also visual content. This capability is particularly useful for tasks like assessing infrastructure damage, visualizing rescue operations, and understanding the broader impact of disasters. 5. TREC Incident Streams [8], developed for the TREC-IS 2018 evaluation challenge, consists of 19,784 tweets labeled for identifying actionable information and assessing the criticality of information. This dataset is specifically designed for enhancing decision-making during disasters by helping emergency responders distinguish between high-priority and low-priority information. 6. Eyewitness Messages [9] focuses on analyzing eyewitness accounts on Twitter. It classifies 14,000 tweets into four distinct categories across events such as floods, earthquakes, fires, and hurricanes. This dataset is particularly valuable for understanding firsthand experiences during disasters, which can provide crucial, real-time information for emergency responders. 7. Disaster Tweet Corpus 2020 [10] consolidates several existing datasets focused on classifying various disaster event types. This resource offers a unified platform for researchers to train models capable of recognizing different types of disaster related tweets. Additionally, [PERSON] et al. [11] integrate eight data sources, yielding a comprehensive collection of 166,100 tweets for informativeness classification and 141,500 tweets for humanitarian classification tasks. These consolidated resources enhance the ability to train robust models that generalize effectively across multiple disaster scenarios. Collectively, these datasets play a vital role in advancing research within the crisis informatics domain.