## 2 Related Work
### Twitter during Natural Disasters  
Different research studies have shown that Twitter is a rich source of information and researchers can use this information to predict the trends, identify the information demands and analyze situations during disasters [15; 26]. The study by [27] discussed the role of Twitter in the dissemination of medical information and misinformation during COVID-19. Data scientists use Twitter data by applying machine learning algorithms and natural language processing techniques to extract comprehensive results for decision-makers. Researchers in [18] use Twitter to predict seasonal influenza; they built a multilingual system called Tweetfluenza that can predict the influenza outbreak using Twitter data. They classified English and Arabic tweets separately based on different keywords. They built a context-aware classifier, where words of the same root and different meanings were removed. They applied machine learning algorithms such as SVM to process the tweets to forecast flu trends. Another research proposed a framework based on a machine learning method to detect the influenza trend in China by collecting a dataset from Weibo, which is similar to Twitter in the context of China [28].  
In a case study, [PERSON] et al. investigate the usage analysis of the Twitter handle of the Mayor of Houston during the hurricanes Sandy and Harvey in August and September of 2017 [29]. In another study, researchers compared machine learning algorithms in the extraction of geo-tagged tweets during emergencies; they used 10 machine learning classifiers on location-oriented disaster-related tweets [30]. A seasonal influenza surveillance system was built on the basis of topics extracted from Twitter [31]. Similarly, a study proposed a multiple layer perceptron with a back propagation model to analyze and predict flu activities from real time data from Twitter [32]. In another study, researchers proposed a hybrid system to extract the salient features of drug abuse health-related tweets by applying linguistic patterns and machine learning classifiers [33].  
Twitter-related research studies used user profiles, keywords and hash-tags to extract the data so that better decisions can be made and the loss of different resources can be minimized. Bilingual language analysis of tweets is performed in the study to check the effectiveness of topic identification and sentiment analysis; they used the COVID-19-related tweets from US and Brazil [34]. Disaster-related information dissemination and exchange is very critical and important. Researchers in [35] proposed a model based on social media usage to predict health trends in the context of the Zika virus outbreak during 2015-2016. They investigated and analyzed the Twitter usage of the government (state, federal and local level). In their paper, they used a mixed methods approach to analyze the social media usage of elected officials of governments and relevant authorities. Several deep learning and transformers-based methods are also utilized to analyze the content. Researchers in [36] similarly used a Bert-based LSTM classifier for classification of tweets of Nebraska floods into nine categories. In [37], the study classified disaster-related tweets into informative and non informative categories using an LSTM model and CNN VGG-16 model by leveraging images and content features. A neural-based approach is developed using a transformers-based Roberta model and feature-based methods for identifying situational and non situational tweets during disasters [38].