# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 2 Principles of Social Security Event Forecasting  
### Problem Definition  
Social security events are characterized by their distinct occurrence in real-world contexts, with critical attributes, including time, location, participants, and semantic themes, that define their nature and impact. In this paper, a social security event is represented by a quadruple \(y=(t,l,a,s)\). Here, \(t\in T\) represents time, \(l\in L\) represents location, \(a\in A\) represents participants, and \(s\in S\) represents semantic themes, where \(T\), \(L\), \(A\), and \(S\) are the object domain, time domain, location domain, and semantic theme domain under study, respectively. It is important to note that these domains need to have a very general meaning. For example, the features contained in the object domain \(A\) apply not only to the subjects of event actions but also to the objects of actions. Similarly, the features contained in the location domain \(L\) can be applied to locating social security events in spaces such as Euclidean space, with spatial coordinates, and non-Euclidean space, such as network nodes. For example, (\"1 June 2010\", \"Germany\", \"Red Cross\", \"Helping Control Epidemics\") represents an event's time, location, participants, and semantic theme.  
When performing social security event prediction, it is necessary to have an input that indicates future events, called event identifiers. Event identifiers contain key information before a future event occurs, i.e., event signs, and also include some irrelevant information [3]. Event identifiers differ from historical event data in that they encompass not only precursors to future events but also potential noise, requiring sophisticated analysis to distinguish relevant signals from irrelevant data. Event identifier data can be represented as \(X\subseteq T^{-}\times L\times F\), where \(F\) represents the domain of features other than the time and location. Data sources for these dimensions include social media, economic databases, and historical records, which each present unique challenges in data collection, consistency, and integration due to variations in data quality and format. If the current time is defined as \(t_{\text{now}}\), and past and future times are defined as \(T^{-}\equiv\{t\mid t\leq t_{\text{now}},t\in T\}\) and \(T^{+}\equiv\{t\mid t>t_{\text{now}},t\in T\}\), the problem of social security event prediction can be described as follows:  
Given event identifier data \(X\subseteq T^{-}\times L\times F\) and historical event data \(Y_{0}\subseteq T^{-}\times L\times F\), social security event prediction is the process of using the given data to output predictions of future social security events \(\widehat{Y}\subseteq T^{+}\times L\times F\). For each predicted future event \(\hat{y}=(t,l,s)\in\widehat{Y}\), it holds that \(t>t_{\text{now}}\).  
It should be noted that not all social event predictions need to predict the four feature domains of time, location, participants, and semantic themes. It is also possible to predict one or more of these features, and since the intrinsic features of time, location, participants, and semantic themes differ, different prediction methods are used for different feature domains.