Crowd or Hubs: information diffusion patterns in online social networks in disasters

The objective of this paper is to investigate the role of different types of users in the diffusion of situational information through online social networks in disasters. In particular, this paper investigates the influence of two types of users: crowd (regular users) and hubs (users with a large number of followers) on the speed and magnitude of information propagation. Effective and efficient disaster response requires rapid dissemination of situational information to improve situation awareness, save lives, and quickly repair damages. The use of social media, such as Twitter, has gained popularity for spreading the situational information in disasters. Little is known, however, regarding the underlying diffusion mechanisms influencing the speed and magnitude of spreading disaster-related information on social media. To address this gap, using tweets related to Hurricane Harvey, this study examined the role of hubs and crowd and the influence of different features such as theme, hashtag, media, users’ location, and the intervention timing of online users on the speed and magnitude of information spread. The results compare the differences in the speed and magnitude of information spread between two diffusion patterns: crowd diffusion (less than 2% retweets from hubs) and mixed diffusion (more than 2% retweets from hubs). The findings suggest that both diffusion patterns can achieve high speed and high magnitude in terms of information diffusion for trending tweets with different features. For mixed diffusion, the speed and magnitude of the tweets are governed by the activities of hubs. The results also show that early intervention of hubs increases the speed of information propagation. Also, in the crowd diffusion, information spread is influenced by both crowd and hubs whose retweets cause tipping points in the information diffusion process at different points of time. The findings imply intervention strategies to better disseminate situational information in disasters. © 2020 Elsevier Ltd