# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 1 Introduction  
Events occur at a specific location and time with a certain topic, which covers a wide variety of real-world phenomena, ranging from natural disasters (e.g., earthquakes, typhoons, and floods), to public health events (e.g., infectious disease epidemics and adverse drug events), to social security incidents (e.g., crime, terrorist attacks, and economic security incidents). No matter what type of event it is, either event location or event content usually varies over time due to the event evolution mechanism. Through mining the spatiotemporal and semantic evolution patterns from historical events, the occurring time, location, and content of the upcoming events can be predicted. Predicting events with accurate information aids the management department as well as individuals in making decisions in a timely manner. With the continuous development of data science and artificial intelligence technology, event prediction has drawn attention from scholars of multiple fields, e.g., social science, computer science, statistics, etc. In other words, event detection emerges as an interdisciplinary hotpot with significant characteristics of collaboration among different domains. As such, there appears an increasing number of studies that are related to event prediction in recent years, which makes it difficult to capture the hot topics as well the future trends in a short time. There are some review papers that survey the event-prediction-related studies [1; 2; 3; 4; 5; 6; 7], among which [PERSON] [4] made the most comprehensive one in the big data era, including the existing techniques, application domains, evaluation procedures, and future directions with regard to event prediction. However, there is a lack of a comprehensive and systematic survey paper that survey and analyze recent literature on event prediction to obtain its evolution patterns over time. With the objective of addressing the aforementioned gap, a few methods can be considered, including bibliometric analysis and scientometric analysis. The bibliometric analysis is focused on organizing and analyzing literature within a specific field to discover the overview and details of this field, which is not conducive to capturing the evolution process, future trends as well as cooperation patterns among interdisciplinary research [8]. The scientometric analysis leverages quantitative methods and indicators to assess the influence and development trends of an author, a document, or a journal in the interdisciplinary research network, helping researchers grasp the hot topics and academic dynamics of the surveyed field [9]. As such, we selected scientometric analysis in this work to survey the event prediction-related literature. Web of Science (WoS) is a comprehensive and widely used online research database that provides access to a vast collection of academic literature, including scholarly articles, conference proceedings, books, and patents, across various disciplines. Web of Science is widely used by researchers, academics, and professionals for literature reviews, citation analysis, and staying up-to-date with the latest research in their respective fields. It is considered a valuable resource for conducting comprehensive and rigorous research across various disciplines. Based on the publications obtained from WoS, the CiteSpace tool of version 6.2.R4, which has been widely used for the scientometric analysis of academic literature, was used to conduct co-occurrence and co-citation network analysis among scholars, publications, institutions, and keywords in order to discover and visualize the central scholars, hot topics, top institutions as well as the future trends in the event prediction field. Those findings are beneficial for scholars to obtain an overview of this field at the frontiers. The rest of the paper is organized as follows. Section 2 summarizes the background knowledge of event prediction. Section 3 outlines the methodology employed for conducting scientometric analysis in this field.