# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Interpretability  
Predictive models of social security events are typically high-risk applications that often come with significant social governance costs. Compared to traditional predictive accuracy, the necessity of providing easily understandable full-chain explanations is somewhat more urgent, as it helps to explain and study human society and enhances the support predictive results can provide to making decisions at the application level. However, given the potential complex connections among the participants, semantic themes, time, and geographical elements behind social security events, research that can provide structured, easily understandable explanations is still scarce. Existing studies often focus only on the accuracy of event prediction, with experiments evaluated mainly based on metrics like accuracy and recall. The prediction results often directly provide a probability value for the occurrence of an event, or a probability value along with information on the time and location of the event. However, they do not provide an explanation of the basis on which the predictive model made its judgment, leading to an opaqueness in its decision-making process and a lack of decision-making rationale, which significantly impacts the practicality of intelligent predictive models.  
Interpretability is crucial for each predictive factor, as it enables a deeper understanding of the data, models, and outcomes that drive social security event predictions.  
\begin{table}
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**No.** & **Study** & **Data Used** & **Feature Signals** & **Prediction Method** & **Prediction Result Form** \\ \hline \multirow{4}{*}{1} & Activism via attention: interpretable spatiotemporal learning to forecast protest activities & Twitter data, social media discussions & Number of tweets, replies, external links, emotional expressions (anger, disgust, etc.) & Deep learning framework (ActAttn), hierarchical attention networks & Forecasting futur protests, identifying significant features and their contributions \\  & Dynamic logistic & & Daily word counts, predictive terms (e.g., “protest”, “police”) & Dynamic logistic regression model, Bayesian variable selection & Forecasting outcome of civil unrest, contextual analysis of underlying reasons \\  & & Information & & Explanatory variables (social intensity, comment numbers, etc.), time series volatility & Prediction of event Models (DLMs), SEP2M and N-SEP2M & Prediction of event \\  & & & & & \\  & & & &  
References [11; 56] use frequent subgraph mining methods to explore the interpretability of social security events at the data level by mining interaction patterns of participants, event types, etc., from encoded event data sources like GDELT and ICEWS. For data, interpretability helps in deciphering the complex interactions and patterns that precede social security events, allowing for more nuanced data analysis and feature selection. References [19; 57] represent elements related to social security events as nodes in graphs and learn based on graph neural networks, representing precursors to events as semantic feature graphs. In interpreting models, understanding the decision paths of graph neural networks and how they relate to event precursors is essential for refining a model's accuracy and generalization capabilities. By combining time retrospection, they form precursor evolution chains, with this predictive model exhibiting both good accuracy and interpretability. Additionally, there is currently no publicly available research directly focusing on the interpretability of social security events at the results level. As for outcomes, interpretability is vital for translating probabilistic predictions into actionable insights, enhancing the trust and utility of the predictions in practical applications.