Doctor of Philosophy (PhD)
Manship School of Mass Communication
During natural disasters, social media can play prominent roles in how publics learn and communicate about disasters, as well as improve disaster communication and operations. The open and flat structure of Twitter platform makes Twitter a suitable platform for disaster communication.
The purpose of this study is to explore the key users, hashtags, and topics in the Twitter networks of natural disasters. In doing so, this study takes a multi-method approach including big data mining and a series of network analyses. This study has collected 9,716,677 tweets during five natural disasters: Louisiana Flood 2016, Hurricane Florence 2018, Hurricane Michael 2018, California Wildfires 2018, and the Indonesia Earthquake 2018. It reveals several important findings. First, results show that government agencies, international organizations, and local media are the most influential user types during natural disasters. The influential users in the Twitter network are not users who have the most number of followers or post the most number of tweets. However, there is a significant relationship between the network positions and hashtag usage. Furthermore, the hashtag analyses also indicate that users can achieve an important position in the network by posting useful content and using hashtags effectively. In addition, with an innovative Geo reverse-coding approach, this study examines the difference in Twitter topics between users within and outside disaster-affected areas. Overall, the whole picture of this project reveals the different patterns of Twitter networks that exist between natural disasters types.
The findings of this study provide insights for developing a network-based multi-method approach in mass communication, specifically in disaster communication. The large-scale Twitter datasets, in combination with text mining, programming, and network analysis methods, can equip researchers to explore disaster communication in the digitally networked society. This study also emphasizes the practical utility of network analysis during natural disasters. The methods and findings can be applied to define key users and create online communities.
Wang, Rui, "Key Users, Hashtags, and Topics: Network Analyses of Twitter During Natural Disasters" (2019). LSU Doctoral Dissertations. 4871.
Available for download on Sunday, March 15, 2026