BackgroundIn the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic.MethodsBased on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics.ResultsResearch findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors.ConclusionThe study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

Large-scale public buildings (e.g., stadiums and comprehensive hospitals) in modern cities provide places for various social activities. However, all of these public places encounter the scenario of large passenger flow and crowd gathering, which is highly likely to induce serious safety problems, such as stampedes. Previous studies have shown that efficient evacuation is an important way to ensure the safety of dense crowds in public places. This study aims to explore the optimization methods to improve the evacuation efficiency of public buildings. Two strategies considering plane partition and multi-floor layout are proposed for plane evacuation and vertical evacuation, respectively. Simulation scenarios and models of large stadiums and high-rise hospitals are established to verify the strategies. The results show that plane partition could effectively shorten the total evacuation time, which is due to the optimization of the initial exit choice of individuals and the avoidance of regional congestion in some evacuation channels or exits. Multi-floor layout optimization is an effective management method to arrange the different features of different floors, which could improve the evacuation efficiency for the whole multi-floor building. This study is helpful for building designers and managers to improve the building space layout design and the daily safety management mode.

Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning. In this work, the cellular automata (CA)-Markov model was adopted by taking into account point of interest (POI) kernel density and station accessibility as driving factors to predict the land use change of station surrounding areas. Then, the land type compositions of different years, temporal and spatial evolution of landscape patterns, and strategies of different metro stations were explored. The results show that the Kappa coefficients of the Zoo Station and the Lu Xiao Station are 87% and 79%, respectively, indicating that the improved CA-Markov model can predict land use changes more accurately by considering POI kernel density and station accessibility. Finally, different optimized strategies based on systematic predictions of land use landscape patterns according to the spatial and temporal distribution of metro stations were proposed. The work provides important references for predicting the impact of new metro stations on land use in the future and guides the adjustment and optimization of land use policy planning.