Abstract: Typhoons are a category of natural disasters whose annual occurrence causes major life and property loss in the Northwestern Pacific region. During typhoon events, social media serve as an effective tool to transmit and acquire disaster information in real time. Texts and photos from social media can be used as a way of crowd sourcing to extract disaster loss information, analyze human behaviors and formulate responses. The dataset presented here consists of social media-based data collected from "Sina-Weibo" microblogs, "WeChat" articles, and "Baidu" news about the typhoon events in 2017, covering Typhoon "Merbok", "Roke", "Khanun", "Haitang", "Mawar", "Hato", "Nesat" and "Pakhar". We mainly collected text data from these social media platforms and websites, which were then cleaned for redundancy and irrelevance. This dataset can be used for deeper disaster information mining of typhoon events.
Keywords: typhoon; social media; disaster reduction; data mining