## 5 Results
### Temporal and Spatial Analysis
The results of the deep learning model (Figure 5) also showed that the number of tweets collected on the day of the earthquake had the lowest number of tweets for the \"Donation\", \"Infrastructure\", and \"Response_efforts\" classes, respectively, with 0.38% (104 tweets), 2.96% (797 tweets), and 6.62% (1779 tweets). The highest number of tweets also belonged to the \"Other_relevant_information\" and \"Injured_Dead_and_Missing_people\" classes, with 80.79% (21,711 tweets) and 9.23% (2482 tweets), respectively. In addition, the tweets collected in the days after the earthquake, amounting to of 0.6% (31 tweets), 4.38% (227 tweets), and 5.01% (260 tweets), related to classes \"Donation\", \"Infrastructure\", and \"Response_efforts\", respectively. In addition, 5.09% (264 tweets) and 84.92% (4403 tweets) belonged to the classes \"Injured_Dead_and_Missing_people\" and \"Other_relevant_information\", respectively. Figure 4: Number of tweets classified using the three, classification algorithms, Naive Bayes, SVM and deep learning, in damage and non-damage classes on each day one week after the earthquake. Overall, most of the messages classified in the three algorithms were in the class \"Other_relevant_information\". Additionally, most of the messages related to infrastructure damages and injured, dead, and missing people were reported on the day of the earthquake. Figure 6 shows the total distribution of classified tweets into the two classes of damage and non-damage at each hour of the earthquake day, by the three Naive Bayes, SVM, and deep learning classification algorithms. In total, 42% (11,293 tweets) of tweets were collected between 9 am and 12 pm. The results of Naive Bayes classification algorithm (Figure 6) showed that 25.61% of the collected tweets from 9 am to 12 noon (2893 tweets) reported damage, and the rest of the tweets (8403 tweets) reported no damage. In addition, 28.39 percent of the tweets (7630 tweets) were collected between the hours of 14 and 19, of which 24.66 percent (1882 tweets) reported earthquake damage and 5748 tweets reported no damage. The SVM algorithm's results (Figure 6) showed that of the 1293 tweets collected between 9 am and 12 noon, 4.77 percent of tweets (539 tweets) reported damage and the rest of the tweets (including 10,757 tweets) reported no damage. In addition, 28.39 percent of tweets (7630 tweets) were collected between the hours of 14 and 19, of which 9.22 percent (704 tweets) reported earthquake damage and 6926 reported no damage. The results of the deep learning algorithm also showed that of the 11,293 tweets collected between 9 am and 12 pm, 10% of tweets (1119 tweets) reported damage, and the rest of the tweets (10,174 tweets) reported no damage. A total of 28.39% of the tweets (7630 tweets) were collected between the hours of 14 and 19, of which 18.6% (1422 tweets) reported earthquake damage and 6208 reported no damage. Figure 5: Distribution of classified tweets in 5 classes on different days, a week after the earthquake (August 24) in the three—Naive Bayes, SVM, and deep learning—classification algorithms. In general, all algorithms had a sudden increase in the number of tweets at the time of the earthquake (10 am). There was also another increase in the number of messages at 4 pm, which may be related to the end of office hours and increased activity on social media. Figure 7 shows the distribution of tweets, classified into five classes, by hour on the day of the earthquake (24 August) for the three--Naive Bayes, SVM, and deep learning--classification algorithms. Examination of the results of Naive Bayes classification algorithm (Figure 7) showed that most tweets classified between 9 am and 12 pm belonged to the classes \"Other_relevant_information\" and \"Response_efforts\" with 79.04% (8929 tweets) and 7.26% (821 tweets), respectively. In addition, tweets for the \"Donation\", \"Infrastructure\", and \"Injured_Dead_and_Missing_people\" classes amounted to 6.94% (785 tweets), 5.01% (566 tweets), and 1.72% (195 tweets), respectively.