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Short video-sharing platforms, such as TikTok, play a significant role in allowing the public to experience natural disasters vicariously, share information, and coordinate peer-led disaster relief efforts. With emerging platforms like TikTok providing experientially immersive content, the role of digital storytelling in stimulating public engagement and emotions remains underexplored. Drawing from narrative persuasion theory and visual storytelling literature, the current study proposes an integrated framework to examine the storyteller, visual frame, and digital presentation characteristics of TikTok videos in predicting social-mediated public engagement and emotional reactions surrounding a recent natural disaster, the 2023 Maui Wildfire. Using visual content analysis and computational methods, the study analyzed 526 TikTok videos and 59,950 associated comments. Results showed that audiovisual vividness consistently predicted all three types of disaster engagement. In contrast, storyteller type, storytelling frame, and other presentation characteristics inconsistently predicted information consumption intention, information sharing intention, and emotional reactions on TikTok. • This study proposes a theory-informed framework of social media public engagement specific to the disaster context. • We investigate how digital storytelling stimulates public engagement and emotions on TikTok during the 2023 Maui Wildfire. • Combining visual content analysis and computational methods, we analyzed 526 TikTok videos and 59,950 associated comments. • Key storyteller, visual frame, and visual strategies significantly predicted information engagement and emotional coping. • Our results have important implications for public relations practitioner to create engaging disaster narratives. [ABSTRACT FROM AUTHOR]

Discussions on social media during major disasters are robust and often have multiple frames of reference. Temporal perspectives, however, are still lacking in current understandings of social-mediated discussions during disasters and crises, but incorporating temporal perspectives can significantly enhance environmental scanning efforts as prescribed in the issues management framework. The purpose of the current research is twofold: to apply and validate the SIR (Susceptible-Infectious-Recovered) model to examine topics' growth over time on social media and to understand how future orientation of social media users (an indicator of temporal distance) affects their construal of a disaster through supervised machine learning. We based our analysis on Twitter discussions during the Texas winter storm in 2021. Results of the study show great fit of the SIR model for topic growth, and that temporal distance affects users' construal of the event in line with core predictions of construal level theory. Theoretical, methodological, and practical implications on social-mediated discussions related to climate change-induced and -intensified disasters and issues management are discussed. • The current study used the infectious disease model to examine topic growth and identify rising topics. • The study incorporated a temporal perspective by modeling topic growth over time and individuals' future orientation. • Applying construal level theory, the study found that the differential growth rate corresponds to the centrality of the topic to the current disaster, with more central topics growing faster. • Temporal distance created a higher level of construal, focusing more on the central features of the event. • The methological approach is particularly relevant to issue monitoring and trend identification in issues management. [ABSTRACT FROM AUTHOR]