## 5 Results
### Temporal and Spatial Analysis
According to SVM algorithm results (Figure 7), it can be seen that all tweets classified between 9 am and 12 pm belonged to the \"Other_relevant_information\" class and only one tweet belongs to the \"Infrastructure\" class. In addition, the deep learning algorithm results (Figure 7) showed that most tweets classified between 9 am and 12 pm belonged to the classes \"Other_relevant_information\" and \"Injured_Dead_and_Missing_people\", with 33.64% (9041 tweets) and 6.58 percent (1770 tweets), respectively. Additionally the tweets for the \"Infrastructure\", \"Response_efforts\", and \"Donation\" classes were 0.98% (265 tweets), 0.67% (182 tweets), and 0.13% (35 tweets) respectively  
Figure 7 shows a sudden increase in the number of tweets, at the time of the earthquake (10 am). In addition, the highest increase was for high-urgency classes. * Spatial topic concentration  
The spatial analysis of social networks could assist us in comprehending the spatial distribution and concentration of emergency topics. For policymakers, this would be helpful for responding to the disaster in a timely manner and with a full understanding of the public concern. Figure 6: Number of tweets classified using the three Naive Bayes, SVM, and deep learning classification algorithms in two damage and non-damage classes in each hour on the day of earthquake. Figure 8 shows the LQ analysis of the tweets' spatial topic concentration at the county scale, classified by Naive Bayes (a), SVM (b), and deep learning (c) in the two classes of damage and non-damage. According to Figure 8a, most tweets collected from the counties of Napa, Yolo, Solano, Contra Costa, San Joaquin, San Francisco, Almeda, and Santa Clara showed damage. However, according to Figure 8b, the results of the SVM algorithm showed that most tweets collected from Napa, Lake, Solano, San Francisco, and Santa Clara counties and other counties reported no damage. In addition, according to the LQ map derived from the deep learning algorithm results (Figure 8c), most damage tweets were reported in Napa, Lake, San Francisco, and Santa Clara counties. In all algorithms, Napa, as a center of the earthquake, was considered as having the primary concentration of damage-related messages in all algorithms. This indicates that our approach has been able to identify the damage well and has considered the earthquake center one of the most affected counties. In addition, San Francisco and Santa Clara counties were considered to be damage concentration counties. This may be due to the high urbanization in these two cities, which has led to increased use of social networks and, consequently, more damage-related messages. It is, therefore, suggested that future research consider the effects of urbanization and eliminate its impact on damage estimation. Figure 7: Distribution of classified tweets, in 5 classes, per hour on the day of the earthquake (August 24) according to the Naive Bayes, SVM, and deep learning classification algorithms. Figure 9 shows the LQ analysis of the tweets' spatial topic concentration at the county scale, classified by [PERSON] (a), SVM (b), and deep learning (c) in the two classes of damage and non-damage. According to the results of the Naive Bayes algorithm (Figure 9a), most of the tweets collected from the Lake, Sonoma, and Santa Clara counties belonged to the \"Injured_Dead_and_Missing_people\" class, and most of the tweets reported by [PERSON], [PERSON], Contra Costa, and [PERSON] were in the \"Response_efforts\" class. Most of the tweets reported in Napa were of the \"Infrastructure\" class, and the tweets in Yolo, Almeda, San Francisco, and San Mateo were in the \"Donation\" class. This indicates that in the center of the earthquake and nearby counties, most of the messages focused on infrastructure  
Figure 8: LQ analysis of the tweets’ spatial topic concentration at the county scale, classified by [PERSON] (a), SVM (b), and deep learning (c) in the two classes of damage and non-damage.