# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 2 Background Knowledge
### Spontaneous Event Prediction  
The spontaneous events are with known or traceable causes but unknown occurrence times, which make up most of the studies in the event prediction field. With regard to predicting such types of events, the causes can be extracted from crowd-sourcing data using machine learning-based classification methods, unsupervised learning methods, knowledge graph-based methods, and multi-technology fusion methods. The typical classification methods such as Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) have been widely used for predicting whether the event occurs (i.e., binary classification problem) or what type of the event (multi-classification problem). [PERSON], et al. [32] integrated heterogeneous data (e.g., Twitter data, blogs, and currency exchange rates) to predict whether civil unrest would happen based on the LR model. [PERSON], et al. [33] proposed a parameter optimization algorithm on the basis of a lasso regression model to predict the event occurrence at a given location during the next time period, which achieved good performance on the datasets of civil unrest events in Brazil and Venezuela. [PERSON] and [PERSON] [34] compared a total of 20 classification models to evaluate their performance for predicting the following event in the business process, where the credal decision tree (C-DT) model performed best. The unsupervised methods without labeling the data mainly include outlier detection methods and clustering-based methods. [PERSON], et al. [35] proposed a spatial-temporal-semantic approach to detect local events using geosocial media data. They first extracted spatiotemporal outliers by measuring the geographical regularities of posting tweets, following which the event content was summarized by adopting the topic modeling method. A case study of the 2014 Toronto International Film Festival (TIFF) was conducted and the results illustrated that 87% of the events can be correctly detected. [PERSON], et al. [36] proposed a genetic programming-based framework that integrated the k-means clustering analysis method to predict the location of the particular events defined by users in a time series. This approach helped e-marketing managers decide where and when to advertise their products by monitoring and predicting the users' searching trends using the Google Trends data that records the keywords and searching time in different countries. [PERSON] and [PERSON] [37] identified the abnormal clusters in the heterogeneous social media graph to predict the event time, location, type, and participants based on the Non-Parametric Heterogeneous Graph Scan (NPHGS) method. [PERSON], et al. [38] applied a spatiotemporal clustering-based method to identify traffic events from Twitter data, where the content of detected events was automatically generated using a list of representative terms. As the knowledge graph emerged in the past ten years, the event knowledge graph has been leveraged for event prediction. The events (or event attributes) compose the nodes and the relationships between events (or between events and their attributes) compose the edges. The causal relationship between events has been mostly investigated through building event causality graphs, based on which the future events are inferred through either knowledge reasoning or semantic web technologies [39; 40; 41; 42]. Despite that, other logic relationships such as sequential relationships, conditional relationships, and subordination relationships were also mined for event prediction with the emergence of the event logic graph [43], where a number of neural networks have been recently adopted to learn the embedded event features with the aim of improving prediction accuracy. [PERSON] and [PERSON] [44] combined the Long Short-Term Memory (LSTM) and the Recurrent Neural Network (RNN) model to predict COVID-19 events. [PERSON], et al. [45] further took spatial information into consideration and constructed spatiotemporal graph neural networks to forecast the COVID-19 trends in space and time.