## 6 Conclusions and Suggestions  
The earthquake severity is among the significant elements of the crisis response and crisis services decision-making procedures. Precise and quick estimates of the severity will assist in lessening the total damage and the number of fatalities following an earthquake. Current severity evaluation techniques manage several different sources of data, which could be split into two major groups. The first group of information is that collected from physical instruments, including seismographs and accelerometers, whereas the second group including information achieved from social monitors, such as eyewitness reports of the earthquake's effects. Therefore, social networks have evolved as a vital information source that could be utilized to boost emergency management. In this regard, this study proposed an approach that applied social media data for an earthquake damage assessment at the county, city, and 10 \(\times\) 10 km grids scale using Naive Bayes, SVM, and deep learning classification algorithms. In this study, binary classification and multi-class classification have been done. Binary classification was used to classify messages into two classes of damage and non-damage so that their results could be used to estimate the earthquake damage. Multi-class classification was used to categorize messages to increase post-crisis situational awareness and to monitor the process of changing conditions over time and to determine the concentration of various topics in different locations. For binary classification and multi-class classification, the SVM algorithm performed better in all the indices, and Naive Bayes algorithm performed poorly in all the indices. The deep learning algorithm also performed moderately in all of the indicators. This may be due to the small size of the training dataset. Therefore, for better evaluation of deep learning performance, the larger dataset is recommended for future research. In social network studies, classification accuracies mentioned throughout disasters vary from 0.6 to 0.9. Therefore, all three classification algorithms performed well. Investigating the temporal dissemination of the topics of the messages being debated will assist in comprehending the disaster evolvement and how the individual perceives the disaster and responds to it. Based on the results of three binary classification algorithms, it can be concluded that most of the damage-related messages were sent on the day of the earthquake and gradually decreased in the days following the earthquake. Therefore, in the early days of the earthquake, people are more concerned about damage and as  
Figure 15: The populations (in units of a thousand) covered by each class in our estimated damage map and FEMA HAZUS loss model map for the (**a**) Naive Bayes, (**b**) SVM, and (**c**) deep learning algorithms. time passes, other issues are more important to them. Based on the results of multi-class classification algorithms, most of the messages classified in the three algorithms were in the class \"Other_relevant_information\". In addition, most of the messages related to infrastructure damages and injured, dead and missing people were reported on the day of the earthquake. This indicates that after the crises that have not been highly destructive, more people follow incident reports and share most of the post-accident alert tips. Based on the results of the hourly temporal pattern analysis, all algorithms had a sudden increase in the number of tweets, at the time of the earthquake (10 am). More attention is being paid to integrating the spatial and content data of social networks. LQ could offer extra perspectives by integrating spatial and content data that go beyond the common point pattern analysis and simple mapping. In this study, LQ was used for identifying area-specific topics. Based on the results of LQ at the county scale in all algorithms, Napa, as the center of the earthquake, was considered as having the greatest concentration of damage-related messages in all algorithms.