## 5 Results
### Temporal and Spatial Analysis  
In this section temporal and spatial analysis are presented. * Temporal analysis Individuals' concerns about a disaster will vary as the disaster evolves. For example, at the beginning of an earthquake, most of the messages may be related to damage and infrastructure, followed by discussions about donations and endowments. The topics shared on social networks are almost an example of the public's thoughts. In this study, temporal patterns of binary classification topics (damage and non-damage) and multi-class classification topics (injured, dead, and missing people; infrastructure; donation; response effort; and other relevant information) via three classification algorithms were investigated. Figure 4 shows the number of tweets classified using three classification algorithms, Naive Bayes, SVM, and deep learning, on two classes of damage and non-damage on each day one week after the earthquake (August 24). Naive Bayes classification algorithm (Figure 4) showed that among the tweets collected on the day of the earthquake, 26.05% of the tweets (4698 tweets) reported earthquake damage and the rest (13,336 tweets) did not report damage. In addition, most of the damage-related messages related to the earthquake day and decreased in the following days. In addition, the results of the SVM classification algorithm (Figure 4) showed that among the tweets collected on the day of the earthquake, 6.61% of tweets (1193 tweets) reported earthquake damage and the rest of the tweets (16,841 tweets) did not report damage. After the day of the earthquake, 5185 tweets were collected, out of the tweets collected after the day of the earthquake, 12.44% of the tweets (645 tweets) showed damage caused by the earthquake and the rest (4540 tweets) reported no damage. The results of the deep learning algorithm (Figure 4) also showed that 13.36% of tweets (2410 tweets) reported damage from earthquakes on the day of the earthquake and the rest of tweets (15,624 tweets) did not show damage. Among the tweets collected after the day of the earthquake, 25.86% of tweets (equivalent to 1341 tweets) reported damage caused by the earthquake and the rest of tweets (3844 tweets) did not show damage. In general, in all three classification algorithms, most of the damage-related messages corresponded to the earthquake day and decreased in the following days. In addition, Naive Bayes extracted the most damage-related messages and SVM has extracted the least damage-related messages. Figure 4: _Cont._Figure 5 shows the distribution of tweets classified in five classes on each day a week before and after the earthquake (24 August) for the Naive Bayes, SVM, and deep learning classification algorithms. According to the results of Naive Bayes (Figure 5), it can be seen that the tweets collected on the day of the earthquake had the lowest number of tweets for the \"Injured_Dead_and_Missing_people\", \"Donation\", and \"Infrastructure\" classes, respectively with 1.54 percent (279 tweets), 4.82 percent (869 tweets), and 5.27 percent (951 tweets). The highest number of tweets also belonged to the \"Other_relevant_information\" and \"Response_efforts\" classes, with 80.71% (14,556 tweets) and 7.64% (1379 tweets), respectively. In addition, among the tweets collected in the days following the earthquake, 0.62% (32 tweets), 0.73% (39 tweets), and 4.32% (224 tweets) belonged to the \"Donation\", \"Injured_Dead_and_Missing_people\", and \"Infrastructure\" classes, and 5.57% (289 tweets) and 88.74% (4601 tweets) tweets belonged to the \"Injured_Dead_and_Missing_people\" and \"Other_relevant_information\" classes, respectively. According to SVM algorithm results (Figure 5), no tweets collected on the day of the earthquake were classified in the \"Injured_Dead_and_Missing_people\", \"Donation\", and \"Infrastructure\" classes. The number of tweets in the \"Other_relevant_information\" and \"Response_efforts\" classes was 99.97% (18,030 tweets) and 7.64% (1379 tweets), respectively, among the tweets collected on the day of the earthquake.