# Earthquake Shake Detecting by Data Mining from Social Network Platforms
## 3 Results and Discussions  
After an earthquake occurrence, there are many data or messages posted to discuss the shaking feeling and disaster information. This study examines the investigation by using the developed decision tree model with other earthquakes and collected data from the PTT BBS. Figure 4 shows the earthquake intensity scale distribution at different places in Taiwan, which is reported by the CWB for the earthquake occurring at 03:57 LT on 6 February 2016 with the seismic magnitude of \(\mathrm{M_{L}}=6.6\). The result shows the largest intensity scale level 7 was derived from Tainan City, which is close to the epicenter, and the intensity scale level 5 was derived from a place near the downtown of Tainan City. The intensity scale reduced away from the epicenter. The intensity scale level 6 was derived at the mountain area of Yunlin County. The result shows that the distribution of the intensity scale follows the depression theorem. Figure 5 shows the same case with the result estimated by the examined model. In this case, there are 1234 BBS posts on PPT and 312 useful messages collected through the two requirements for analysis. There are many posts for analysis and many results are obtained for a local area. In this investigation, we selected the largest shaking scale level as the scale level of the local area. The result shows the largest intensity scale level 6 to be observed in Tainan City, intensity scale level 5 in Kaohsiung City, and scale level 4 in Maioli, Changhua, Yunlin, Chiayi, and Pingtung counties. The eastern part of Taiwan represents the scale level 2 and 3 at Yilan and Hualien cities, respectively. The place shows up without a scale value at Taitung County, which means that we cannot collect any posts or identify the post to describe this place. The result of Figure 5 shows good agreement with Figure 4, except for the largest scale, which is less than 1 in Tainan City. The difference in the largest intensity scale in Tainan City is associated with many factors, but the possible reasons could be the emotional state and the building structure state. However, the result indicates the true emotional reaction and the observed condition of the damage, which is a case of good agreement that is officially reported for analysis by using the proposed method. Figure 4: The intensity scale distribution reported by the CWB for the earthquake occurring on 6 February 2016. Figure 6 shows the same result as Figure 4 but for another earthquake event that occurred in Nantou County on 27 March 2013 with a seismic magnitude of \(\rm M_{L}=6.2\). The result indicates that the largest intensity scale level 6 occurred in Nantou County and the next scale level 5 also occurred mainly in Nantou County with few occurrences in Taichung City and Miaoli County. The intensity scale level 4 occurs in Taichung City, Changhua, and Yunlin counties. The intensity scale levels 3 and 2 occurred in the eastern part of Taiwan. Figure 7 shows the result derived by using the proposed method. In this event, there were 775 posts and 163 messages collected to satisfy the two requirements to be used for analysis. The result shows that the largest intensity scale level 5 is observed at Taichung City, and the intensity scale level 3 is observed at Nantou County (central Taiwan). Furthermore, the intensity scale level 3 was observed in the northern part of Taiwan and Tainan City. The intensity scale level 3 was observed in the southern and eastern parts of Taiwan. Comparing Figure 6 with Figure 7, the result shows a fair agreement with that of the official report (Figure 6), except for the place of the largest intensity scale. The under-estimated error could be due to the lower population at the mountain area in Nantou County and a smaller seismic shaking affect at the downtown area. Therefore, the estimated value is smaller than that of the official report. The other possible reason could be the severe damage caused by the earthquake to the network, which hindered users from posting messages.