# Earthquake Shake Detecting by Data Mining from Social Network Platforms
###### Abstract  
This study used social media posts of the related effect of earthquakes to derive seismic shake scale distributions in regions of Taiwan and compared it with the regional seismic scale reported by the Central Weather Bureau (CWB) of Taiwan. This study conducted a context searching to scrawl the relationship phrase on the social media network platform, PTT bulletin board system (BBS), to detect the earthquake shake scale using the keywords of the context. In this investigation a decision tree model for analyzing the semantic words from the context of the target event to detect the earthquake shake scale was devised. The results indicate that we can pick out the keywords to use to detect the earthquake shake scale at about 85%. Furthermore, the results of the derived shake scale show that the four studied cases are in a good agreement with the presented news from the CWB of Taiwan. In this study, the author attempted to develop a quick earthquake shake scale detection model by semantic analysis of the collected earthquake disaster information reported on the social media network platform. social network; data mining; decision tree; genetic algorithm +
Footnote †: journal: Medical  
analysis, and prediction in natural disasters. Big Data and information technology provide meaningful and effective solutions in natural disaster management. [PERSON] et al. (2014) reported that because a large amount of data is recorded at the platform, data could be collected from the social media network and analyzed. Moreover, [PERSON] (2015) studied the effect and disaster of earthquakes by using the real-time information on Twitter to offer information for supporting information for rescue activities. [PERSON] et al. (2016) suggested that messages posted on the social media from disasters are useful to estimate the condition of the disaster for human beings and buildings. Meanwhile, [PERSON] also made a prediction for the seismic scale by using the data collected from the social media network Twitter (2016). [PERSON] et al. (2017) used the Twitter real-rime tweet flood reports and geo-parsing to generate real-time crisis mapping of natural disasters. [PERSON] and [PERSON] (2018) showed real time analytics based on social media data to detect and monitor behaviors. [PERSON] et al. (2019) developed a system to filter and classify social media messages related to emergencies, disasters, and humanitarian crises to tag up in a world map. [PERSON] et al. (2020) analyzed social media data to develop a system to provide analysts with an interactive visual spatiotemporal analysis and spatial decision support environment that assist in evacuation planning and disaster management. Furthermore, [PERSON] et al. (2018) determined the epicenter by using the real-time messages on Twitter. They took the user on Twitter into a sensor, and applied the particle filter method to estimate the location of the epicenter and send a warning message by e-mail to registers on Twitter in Japan. [PERSON] et al. (2022) tried to establish a concept for a community-based map creation process based on \"human sensors\" to estimate the earthquake intensity. In China, [PERSON] et al. (2023) proposed a semi-real time disaster information collection method by using the geographic information system (GIS) to analyze the Global System for Mobile Communications (GSM) messages. Today, there are many social media network platforms, such as Facebook, Twitter, Plurk, Tumblr, Emote.in, Squeelr, Jaiku, identi.ca, and PTT. In this study, we analyze the data collected from the discussion posts on PTT. The PTT system allows real-time interaction that makes it a popular social media bulletin board system (BBS) in Taiwan. PTT was established in 1995 with the aim to provide a public, free, and liberty of speech platform based on academic research and noncommercial conduct. PTT has many discussion boards on specific topics, and the user can join the discussion in the related discussion board according to his/her interest.