## 2 Literature Review  
With the growth of smartphones and the popularity of social networks, social media gives a new critical source of data for crisis management. Much of the current literature on the use of social network data in natural disasters has concentrated on different facets, including earthquake detection, situational awareness, and damage estimation [4]. The detection of earthquakes using social networks is an especially popular field of research. [PERSON] et al. [25] offered a system that applied social network users as earthquake detectors. They concluded that their system notification was sent much quicker compared to the warnings system by the Japan Meteorological Agency. [PERSON] et al. [26] demonstrated how instrument-based event detection and estimation of earthquake location and magnitude could be supplemented by Twitter data. [PERSON] et al. [27] showed that by using clustering algorithms, the system facilitates instant identification of probable events. Social networks would allow catastrophe managers to understand what is going on in cases of catastrophe. Crisis managers need actionable disaster-related data to make sense of the catastrophe and promote decision making, strategy formation, and execution of responses. Previous researches have used different methods, such as supervised and unsupervised classification kernel density estimation (KDE), to produce spatially appropriate data for situational awareness and improved response to disasters [28]. Among supervised algorithms, Naive Bayes and SVM are more widely used [29], and among unsupervised algorithms, latent Dirichlet allocation (LDA) algorithms are more widely considered [30]. Different studies have used different methods to classify text messages. In [PERSON] et al. [31], the desirability of the SVM linear kernel for the classification of crisis-related messages was verified across the RBF (Radial Basis Function) kernel, the polynomial kernel, and the logistic regression. They assessed the models being trained by determining the precision (P), recall (R), and F1 metrics. [PERSON] et al. [32] applied deep neural networks and Naive Bayes to classify Twitter messages throughout crises. [PERSON] et al. [33] introduced a mixed approach for determining individuals at risk both during and after a crisis using real-time classification. To evaluate the model's efficiency, they applied three methods, including Naive Bayes, decision tree, and SVM. [PERSON] and [PERSON] [34] developed an automated tool that detected social network tweets associated with disasters. They applied Naive Bayes, classification and regression trees (CART), SVM, and CNNs. [PERSON] et al. [35] investigated information trends in social networks during and after the crisis, including how various kinds of information evolved over time and how information was disseminated in the social networks. [PERSON] et al. [29] evaluated the SVM and the Naive Bayes algorithms for classifying crisis-related messages and concluded that the SVM had better performance. [PERSON] et al. [36], regarding the informational significance of social networks, noted that tweets relevant to crises differ significantly in their utility for handling crises. They used machine learning methods to differentiate between related and non-related messages. [PERSON] et al. [7] examined messages from social networks to handle crises. They demonstrated that there is a strong relationship between messages relevant to a crisis, which include photos and their distance to the incident. Therefore, the photo in a social network message may represent an indicator of the high likelihood of related content. These results could be applied to improve information exploitation from social networks in order to increase situational awareness. [PERSON] and [PERSON] [28] examined the variability of spatial topic concentration before, during, and after the crisis.