Understanding the dynamics of information diffusion through data-driven social network modeling for the 2012 U.S. drought and wildfire

Abstract

Social network plays a critical role in risk communication diffusing information in near real time. Disaster-affected communities utilize their social network to report catastrophic damages and increase the perceived risk of the ongoing disaster by non-affected communities, which enhance their willingness to donate and support emergency aids to the affected communities. Previous studies have focused on social network structure or information diffusion separately. This study strives to reproduce the social response to natural disasters aims integrating the two aspects of social network structure and information diffusion. This study focuses on two classical and catastrophic U.S. disasters, such as 2012 flash drought and wildfire, to establish the social network during these two disasters and understand difference in the patterns of the risk communication within the data-driven social network and random social network (e.g., (the equal chance/importance of a nodes). Random social network is made from the LFR benchmark algorithm using the properties of the data-driven network, including node number, degree distribution, community distribution, and average degree. This study leverages over 120,000 (53,000) tweets that contains a term, drought (wildfire). In this study, a Susceptible-Infected-Recovered (SIR) model is employed to simulate the information diffusion patterns using the data-driven and random social network. After fitting SIR model with the Twitter data using these two social network-based simulations, this study aims to assess 1) the impact of the structure difference on risk communication and 2) the impact of influential users in different social network structures. Result shows that the trained SIR model using the data-driven social network reproduced the observed information diffusion patterns for the 2012 drought and wildfires but with relatively higher uncertainty in the information diffusion pattern for wildfires. The SIR model simulation with data-driven social network shows a faster information diffusion pattern with a higher information reach rate than that with the random social network. In closing, this study discusses limitations and opportunities of next-generation social dynamic modeling for natural disaster risk communication. This study highlights the value of an interdisciplinary approach in improving risk communication and developing a more efficient and effective mitigation policies for not only droughts and wildfires and other natural disasters.