# Earthquake Shake Detecting by Data Mining from Social Network Platforms
## 4 Conclusions  
In the past, posts and messages about earthquakes were considered to be of meaningless content. This study proposed an approach that can be used to estimate the earthquake intensity scale and confirms the possibility of the damage caused by the earthquake via factual analysis of users' messages from the social media network. The messages by users from the social media network provide accurate conditions of earthquakes for analysis and could be different from seismographs. We are currently in the Internet era where many people post short messages on the social media network when an earthquake occurs to report their emotional states and the damage states of ornaments or cupboards. Some studies have also estimated the earthquake intensity scale by surveying social media network, which are mostly based on Twitter [21]. To extract disaster information effective for scale classification from the useful reported messages, the decision tree model was designed. This is the first study to estimate the earthquake intensity scale by the Chinese semantic from the social media network PTT. The PTT system does not have the location information of users, which increases the difficulty of the analysis in this study. Therefore, the data of the messages should be composed of two classifications of location and disaster information. This study analyzed the posted messages from the PTT website about earthquakes by using the data mining method to derive the earthquake intensity scale over Taiwan and compared it with the official report by the CWB. The results showed good agreement with the report of CWB for most cases in this study. However, this aspect is mainly concerned with severe seismic events because light seismic events do not pose a serious threat to the social media network. In this study, the results showed an overestimation of certain intensity scale levels compared to that of the CWB official report. This result is attributed to the shaking feeling, building structure, and the local geological conditions. However, we attempted to respond to the accurate disaster information of the region. The author hopes that this study provides some useful information, and thatthe results are shared with society to help emergency stakeholders and disaster management. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Regions** & **Cse 1** & **Cse 2** & **Cse 3** & **Case 4** & **Case 5** \\  & **M6.2** & **M6.4** & **M6.6** & **M5.7** & **M5.7** \\ \hline Keelung & 2–3 (2) & 2 (2) & 1 (1) & 3–4 (3) & 0 (0) \\ Yilan & 2–3 (2) & 3–5 (2) & 1–2 (2) & 3–6 (3) & 1 (X) \\ Taipei & 2–3 (3) & 3 (4) & 1–2 (2) & 3 (3) & 0 (1) \\ New Taipei & 1–2 (3) & 2–4 (2) & 1–2 (3) & 2–4 (3) & 0 (1) \\ Taoyuan & 2 (2) & 3–4 (3) & 2 (2) & 3–4 (3) & 0 (0) \\ Hinchu & 3 (2) & 3 (3) & 2 (2) & 3–4 (2) & 1 (1) \\ Maoli & 3–5 (3) & 3 (2) & 2–3 (3) & 2–3 (3) & 1 (1) \\ Taichung & 4–5 (5) & 3 (4) & 3 (3) & 1–2 (2) & 1–2 (2) \\ Changhua & 4–5 (4) & 2–3 (3) & 3–4 (4) & 1–2 (2) & 2 (2) \\ Nantou & 4–6 (3) & 2–5 (2) & 2–3 (3) & 1–2 (X) & 1–2 (1) \\ Yunlin & 4 (X) & 3 (3) & 4 (3) & 2 (1) & 2–3 (2) \\ Chiayi & 3–4 (2) & 3 (3) & 5 (4) & 1–2 (1) & 3 (3) \\ Tainan & 2–4 (3) & 2–3 (3) & 5–7 (7) & 1 (1) & 2–6 (5) \\ Kaohsiung & 2 (2) & 2 (2) & 5 (5) & 1 (1) & 2–4 (4) \\ Pingtung & 1–2 (2) & 2–3 (2) & 3 (3) & 1 (X) & 1–3 (2) \\ Taitung & 1–2 (X) & 1–3 (2) & 3 (X) & 1 (X) & 1–2 (X) \\ Hualien & 2–3 (2) & 3–6 (5) & 2–3 (2) & 1–4 (1) & 1 (X) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summarization of the scale level for the model estimated and the CWB official reporting. Funding:This study was supported by the Grants of the Ministry of Science and Technology MOST107-2111-M-275-001. The author greatly thanks the PTT for providing data online. Meanwhile, the author thanks the anonymous reviewers for their insightful comments, which significantly improved the paper. Moreover, the author would like to thank C.F.H. for help in analyzing data. Furthermore, this study was supported by the Grants of the Ministry of Science and Technology MOST107-2111-M-275-001.