## 1 Introduction  
Earthquakes occasionally happen unexpectedly with little alert; therefore, crisis management could be a challenging job [1]. Disasters or significant events, such as earthquakes, lead to enormous harm to life and assets [2]. The capability to quickly assess the spatial distribution of damage across a vast region subsequent to a considerable earthquake is crucial for effective disaster response. It also could be effective for the assessment of losses and public communication [3]. Residents in the impacted areas are defenseless and demand sufficient assistance and help from rescue workers (e.g., authorities' organizations, and non-governmental organizations). The number of the disaster-affected people, thus, has an important effect on the proper timing to implement of rescue and assistance activities [4]. Authorities demand precise data regarding the spatial distribution of damages as soon as possible so that they will be able to dispatch assistance rapidly to suitable regions [3]. Nonetheless, collecting these data rapidly is difficult, and significant amounts of labor and assets are required to gather data in near real-time via conventional information capturing activities, including observing inquiries, telephone reports, or remote sensing images [1]. Traditional strategies for handling crises to deal with social and financial casualties and mitigate the consequences of a catastrophe undergo a variety of deficiencies, such as extreme temporal delays or restricted temporal and spatial resolution [5]. Regardless of the existence of advanced satellite detectors, able to observe proper spatial and spectral resolution, remote sensing image gathering and interpretation demand costly resources, including costly instruments and sophisticated data preparation devices, also in addition to fine weather circumstances. The remote sensing images need to be obtained and interpreted quite quickly to help the rescue groups, which is not often feasible with conventional acquisition and processing approaches [6]. Social media has grown to a main channel of communication over the last few years. In crises, individuals not only use social media to collect and exchange information but also to generate new data [7]. Social media offers several benefits and concerns to all domains of application and research that are associated with applying social media content for enhanced emergency management [8]. As a result of progress in technology and the pervasive use of smartphones, social networks are continuing to develop and are often used to disseminate, exchange, and gather information throughout crises [9]. A specific benefit comes from local people in the surrounding of the incident using social networks to express a specific view of reality. In the aftermath of crises such as earthquakes, social media users publish messages about the potential damage, which can be used to estimate the damage. Mostly, they are able to give unique information from their region, which is not yet given by any other authority [7]. Social network data, in contrast to conventional data, facilitate low-cost data gathering on a unique temporary resolution during emergency situations; therefore, these kinds of data cannot be neglected in crisis-related deciding efforts [1]. In damage assessment, different analyses perform differently in distinct scales (spatial units). Therefore, it should be determined on which scale the analysis for each kind of data source should be performed in order to have better performance. It will then be possible for decision makers to choose what kind of data to analyze at each scale. This makes the results closer to reality. Classification algorithms are needed to estimate the earthquake damage based on social network data [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Another issue that affects the damage estimation is the selection of the appropriate classification algorithm.