The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public’s sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people’s discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences.

Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner.

Social media has been a vital channel for communicating and broadcasting disaster-related information. However, the global spatiotemporal patterns of social media users’ activities, interactions, and connections after a natural disaster remain unclear. Hence, we integrated geocoding, geovisualization, and complex network methods to illustrate and analyze the online social network’s spatiotemporal evolution. Taking the super typhoon Haiyan as a case, we constructed a retweeting network and mapped this network according to the tweets’ location information. The results show that (1) the distribution of in-degree and out-degree follow power-law and retweeting networks are scale-free. (2) A local catastrophe could attract significant global interest but with strong geographical heterogeneity. The super typhoon Haiyan especially attracted attention from the United States, Europe, and Australia, in which users are more active in posting and forwarding disaster-related tweets than other regions (except the Philippines). (3) The users’ interactions and connections are also significantly different between countries and regions. Connections and interactions between the Philippines and the United States, Europe, and Australia were much closer than in other regions. Therefore, the agencies and platforms should also pay attention to other countries and regions outside the disaster area to provide more valuable information for the local people.