Earthquake Impact Analysis Based on Text Mining and Social Media Analytics
This work addresses the need for timely disaster assessment using social media, but it is incremental as it applies existing text mining methods to earthquake data.
The authors tackled the problem of early earthquake impact analysis by developing a text mining approach to analyze social media data from Sina microblog, showing that public opinion trend and sentiment analysis can estimate social impact for decision-making in rescue operations.
Earthquakes have a deep impact on wide areas, and emergency rescue operations may benefit from social media information about the scope and extent of the disaster. Therefore, this work presents a text miningbased approach to collect and analyze social media data for early earthquake impact analysis. First, disasterrelated microblogs are collected from the Sina microblog based on crawler technology. Then, after data cleaning a series of analyses are conducted including (1) the hot words analysis, (2) the trend of the number of microblogs, (3) the trend of public opinion sentiment, and (4) a keyword and rule-based text classification for earthquake impact analysis. Finally, two recent earthquakes with the same magnitude and focal depth in China are analyzed to compare their impacts. The results show that the public opinion trend analysis and the trend of public opinion sentiment can estimate the earthquake's social impact at an early stage, which will be helpful to decision-making and rescue management.