## 5 Experiment and Case Study
### Case Study:Tweet Summary of the \"COVID-19 Pandemic\" Event  
In 2020, COVID-19 is undoubtedly one of the hottest topics. The COVID-19 pandemic has exposed many serious errors in global public policy preparation and response. Although it has been affected by other epidemics and pandemics for more than a decade, many countries have failed to incorporate lessons learned into their pandemic preparedness and response plans. One of the main factors is that we lack effective real-time event-related intelligence collection systems which can provide a summary of updates related to the COVID-19 pandemic in all countries. Hence, we cannot learn from the successful experience of other countries in fighting the epidemic in time. Since the COVID-19 outbreak, very few studies that respond to practical needs, such as through rapid advice guidelines or application modules, have been conducted. However, many professionals, such as health workers, are in urgent need of effective ways to help people improve their well-being during this pandemic. According to the US Centers for Disease Control and Prevention (CDC), information on the outbreak of the coronavirus disease may increase the levels of distress among specific population--especially in those who were already struggling with mental health issues before the pandemic. They may be likely to experience higher levels of worry and stress regarding issues surrounding COVID-19. This trepidation may worsen over time and cause a long-lasting mental disturbance [54]. We wish that the proposed system can help the professionals detect and track COVID-19 events at an early stage as well as monitoring the emergency events within specific groups. The view is taken, therefore, in this case study, we combine our developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing COVID-19 events and sub-events. In Figure 4, it can be found that there are many detected hot-event topics around COVID-19 in 2020. In order to satisfy users' information needs, we pick up all of the real-time event topics with a comprehensive spatio-temporal viewpoint. By analyzing the events detected by our system, the spatial and temporal impacts of the emerging events can be estimated. Therefore, we first build a representation of the spatial distribution by mapping the summary of events related to COVID-19 in the world in 2020, as shown in Figure 5. After that, in terms of temporal aspects, we constructed a storyline retrospection mechanism that demonstrates the story telling capability for users to understand the context of event development in a timeline, as illustrated in Figures 6 and A1-A3. Figure 4: Detected event topics related to COVID-19 in 2020. In Figure 5, we present hot topics in the form of a map to illustrate global events to understand the spatial impact of the COVID-19 pandemic on the world. Furthermore, in this work, we developed a storyline retrospection mechanism that demonstrates the story telling capability for users to understand the context of event development in an efficient way. It can be used to identify events and their relations under an event topic and then composes a storyline which gives users the sketch of event evolution in the topic, by computing daily related tweets and summaries of formulated event topics. The main storyline construction removes irrelevant events and presents a main theme that exhibits the sequence of events in a hot topic. In Figures 6 and A1-A3, we can see the storyline of COVID-19 in 2020, from the beginning of the outbreak to the emergence of the final vaccine. Figure 7 shows the example resulting summaries of COVID-19 event related tweets in 2020, starting with a new virus related to the SARS outbreak that may cause Chinese health officials to discuss on social media Pneumonia. From the time trend shown in Figures A1-A3, when the COVID-19 pandemic first broke out, social media were mainly discussing where the pandemic occurred and how to properly protect themselves from the pandemic.