[Objective] This paper analyzes the trending topics generated by users from disaster-affected areas and those by users from non-disaster areas at different stages of a disaster, aiming to discover the evolution of topics. [Methods] Firstly, we used geo-tags and users’ profiles to decide their locations. Then, we proposed a framework based on topic-word co-occurrence and community detection to identify trending topics, calculate topic strength and analyze topic evolution. Thirdly, we used alluvial diagram to visualize the evolution of these topics. Finally, based on situational awareness theory, we compared the macro and micro-evolutionary patterns of trending topics between the two user groups. [Results] During a disaster, the affected users mainly published tweets on physical environment, while the non-affected users tended to express their emotions on Twitter. After a disaster, the affected users mainly published emotional topics, while the non-affected users posted tweets on built environment and physical environment. [Limitations] Deciding a user’s geographic location based on his/her profile might not be reliable. More research is needed to optimize the measurement of topic strength. [Conclusions] The affected and non-affected users show different topic preferences at various stages of a disaster, which helps the related agencies identify peoples in need more effectively.
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