[Objective] This study aims to discover information topics and identify real-world events that stimulate public discussions. It helps us establish timely responses and reduce risks. [Methods] We first constructed a co-word network to detect communities representing topics. Then, we calculated the document topic vectors based on the overlaps between the document words and topic community words. Third, we decided topic popularity time series according to the document time. Finally, we used the STL to decompose topic popularity time series and employed the 3σ rule to detect anomalies. We identified real-world events stimulating discussion by examining high-frequency words and highly correlated documents at anomalous time points. [Results] We examined the new model with posts from Sina Weibo about the heavy rainstorm in Henan. We discovered topics related to disaster situations, emergency management, and social response. Anomaly detection and analysis show that the topics about disaster situations received the highest public attention, with rainfall warnings and flood control actions being hot events. In emergency management, rescue and relief efforts and accident investigation can stimulate discussions. Regarding social response, stories of victims' mutual aid and public donations attract attention. [Limitations] The dataset of this study is relatively small, so we have to manually set the threshold of anomaly detection. An automatic method is needed for larger datasets. [Conclusions] Anomaly detection in topic time series can identify the trending events on social platforms. In crisis response, government agencies need to address rescue, prevention, and recovery aspects, issue timely warnings, provide information on disaster relief and accident investigations to address public concerns, and guide positive or healthy public opinion by promoting rescue, mutual aid, and donation activities.