# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Predictive Factors  
#### 3.1.1 Time Factor  
The time element is the most fundamental factor in social security event prediction. The idea behind such methods when predicting social security events is to macroscopically focus on a particular country or city to determine whether social security events will occur in a certain timeframe or provide the probability of a social security event occurring. Based on different granularities of prediction time, this paper categorizes them into three types: a. focusing on a future time period; b. focusing on a future day; c. focusing on a specific future moment. Predicting future time periods is used for medium- to long-term planning that focuses on overall trends. It is suitable for large-scale predictions such as traffic management and public safety. Predicting specific future days is the realm of short-term  
Figure 4: Research framework of social security event prediction.  
Figure 3: Keywords in event prediction and related fields.  
forecasts, widely applied in crime prevention and traffic accident predictions. Predicting specific future moments is useful for precise time-point forecasts, ideal for sudden incidents and high-risk scenarios like terrorist attacks and violent events.  
Studies that focus on a future time period involve specifying a time window in the future to predict whether social security events will occur within that timeframe. The size of the time window can be chosen by researchers based on specific task requirements. For example, [PERSON] et al. [4] predicted the probability of protest events occurring every three days during the Egyptian unrest. They trained a random forest classification model on event data from Recorded Future and made predictions using Twitter data from the period before the unrest events occurred. [PERSON] et al. [5] predicted weekly flu outbreaks in various US states using a seven-day time window for disease outbreak prediction. [PERSON] et al. [11] proposed a model that estimates the probability of social security events occurring in the next month in five countries, including Brazil, by mining structured features from media information through frequent subgraph extraction and using logistic regression.  
Predictions that focus on a specific future day involve specifying several specific dates in the future to predict whether social security events will occur on each of those days. The selection of specific dates can be a single day in the future, consecutive days in the future, or non-consecutive days in the future. [PERSON] et al. [12] employed graphical and sequential neural networks to predict next-day events in Hong Kong based on historical data, achieving an accuracy of 0.85. [PERSON] et al. [13] used a non-parametric heterogeneous graph scan model that considers the entire heterogeneous network for event detection to predict internal conflict times in four countries, including Argentina, and to determine whether unrest events would occur on specific dates in advance. Additionally, the \"Brazil Spring\" dataset has received significant attention, with [PERSON] [14] and [PERSON] [15] providing the probability of protest events occurring every day in June in Brazil. [PERSON] et al. [16] predicted insurgency events in Iraq by selecting a day each month using an implicit Markov model and providing the probability of insurgency events occurring on each date.  
Predictions that focus on a specific future moment involve specifying that specific moment in the future, such as 10 December 2023, at 3:30 p.m., for predicting social security events. Current known prediction methods have not achieved this level of time granularity yet. However, for social security events that are premeditated and planned, relevant information for specific moments can be captured through media information extraction methods [17; 18; 19].