## 4 Methodology
### Classification  
Social networks have a crucial function in the dissemination of information throughout crises. Regrettably, the enormous amount and diversity of data produced on social networks make it difficult to manually search through this content and specify its relevance for damage estimation. Though, with many social network opportunities, real challenges arise, such as handling such large quantities of messages, which make manual processing extremely insufficient. The information overburden throughout crises could be equal to the lack of information for the emergency management. Therefore, classification algorithms can be used to solve these problems and extract useful information. In this study, binary classification and multi-class classification have been done. Binary classification is used to classify messages into two classes of damage and non-damage so that their results can finally be used to estimate the earthquake damage. Multi-class classification is used to categorize messages to increase post-crisis situational awareness and to monitor the process  
Figure 2: Flowchart of the study. of changing conditions over time and determine the concentration of various topics in different locations. Previous research has used SVM, Naive Bayes, and deep learning algorithms to classify crisis-related messages and performed better [31; 32; 33; 34]. In this regard, in this research, SVM, Naive Bayes, and deep learning algorithms have been used. These three algorithms are described below. * Naive Bayes  
The Naive Bayes algorithm is regarded as a Bayesian algorithm. The Naive Bayes is a big-bias, small-variance classification algorithm, and even with a small set of data, it can build a good model. It is so simple to use and affordable to compute. A Naive Bayes classification algorithm is a simplistic probabilistic classifier based on the theory of Bayes (from Bayesian statistics) with powerful (Naive) hypotheses of independence. A Naive Bayes classification algorithm assumes that the existence (or absence) of a specific element of a class is irrelevant to the existence (or absence) of any other element [39]. The presumption of independence greatly makes simpler the computations required to create the probability model for the naive Bayes. The Naive Bayes has the benefit of not demanding hyper-parameter tuning as a comparison with other methods. In addition, [PERSON] et al. [40] showed that the results of tweet classification for disasters extracted with The Naive Bayes are analogous and often better than those acquired with other more complex algorithms used with predefined variables. * SVM  
The SVM is a supervised method that is applied for classification and regression analysis. The SVM removes the requirement to reduce the space of the higher feature dimension and has an automated variable-adjusting property that is appropriate for text classifying. This is a functional-based classification algorithm that is created on the basis of decision planes specifying class boundaries [41]. The fundamental concept is to discover a hyperplane that segregates the d-dimensional information efficiently into its two classes. Since all of the data are not always linearly distinguishable, the SVM comprises the concept of a kernel-induced feature space that projects the information into a higher-dimensional space in which the data are easier to separate [39]. After optimizing the SVM parameters, a linear the SVM kernel has been used. * Deep learning  
An emerging availability of various labeled datasets recently enabled the successful application of deep learning for crisis-related messages classification. The deep learning  
Figure 3: The process of pre-processing textual data [5]. algorithm used in this study is, in fact, a multi-layer feed-forward artificial neural network algorithm that uses a stochastic gradient descent approach via back-propagation to predict labels of classes. This network can contain a large number of hidden layers, including layers with rectifier neurons and maxout activation functions.