The objective of this study is to infer the cascading effects between disruptive events in disasters using content coding and imagery representation techniques on social media videos. It is well recognized that disaster events are cascading due to the dynamic interactions between natural and built environments. Understanding why and how the failures of natural and built environments such as road and vehicle damages cascade is critical for retrospective investigation and disaster adaptation. Social media provides high-volume and real-time data, which has been broadly used in disaster management. What's more, the immense availability of social media videos, which record the facts of disaster events with high spatial and temporal resolutions, provides a unique opportunity to review and uncover the cascading process in disasters. Here, we propose an analytical method from two theoretical perspectives: content understanding and the philosophy of cascading effects. First, we establish a content classification system to code social media videos with respect to specific disaster events. Then, an imagery representation approach is developed to capture the cascading effects based on the observed relations from the social media videos. To examine the application of these methods and to derive new insights for the cascading effects, we leverage empirical social media video data collected from Weibo, a popular Chinese microblogging platform, during Typhoon Lekima. The coding results show that social media videos contain a wealth of information regarding disaster impacts, enabling us to unveil the occurrences and processes of the natural and built environment failures. By establishing cascading relations among disaster failures, we can present a whole picture of the complex disaster events induced by typhoons. The proposed method and findings of this study can contribute to not only computational modeling of cascading failures within natural and built environment, but also informing the design and planning of cities in adapting to future disasters.