Abstract
Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.
Highlights
With the popularity of mobile devices and the development of the network infrastructure, social media has quickly integrated into people’s lives
Few researchers have considered the public emotional information contained in social media as an attribute of disaster-related geographic information to aid in disaster mitigation
We regarded the fine-grained public emotional information extracted from social media as an attribute of geographic information to assist in disaster mitigation
Summary
With the popularity of mobile devices and the development of the network infrastructure, social media has quickly integrated into people’s lives. Compared with traditional disaster information collection methods, social media has the characteristics of real-time information provision and low cost These data contain a lot of geographic information (such as location, time, and other attribute information), which is very important for disaster mitigation. Few researchers have considered the public emotional information contained in social media (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. As of Q3 2018, Chinese social media platform Sina micro-blog had over 431 million active monthly users [6] When disasters occur, this will generate a lot of disaster-related data. We used a Sina micro-blog and took an earthquake disaster as an example to describe how the framework we built extracted fine-grained public emotions and used them to serve disaster mitigation How can the fine-grained emotional information contained in these data be extracted more accurately? (3) When these fine-grained emotions are extracted, how can they be regarded as an attribute of disaster-related geographic information to assist disaster mitigation? In this paper, we used a Sina micro-blog and took an earthquake disaster as an example to describe how the framework we built extracted fine-grained public emotions and used them to serve disaster mitigation
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call