Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
Social security event prediction can provide critical early warnings and support for public policies and crisis responses. The rapid development of communication networks has provided a massive data analysis base, including social media, economic data, and historical event records, for social securi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/580 |
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| _version_ | 1832589277408526336 |
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| author | Nuo Xu Zhuo Sun |
| author_facet | Nuo Xu Zhuo Sun |
| author_sort | Nuo Xu |
| collection | DOAJ |
| description | Social security event prediction can provide critical early warnings and support for public policies and crisis responses. The rapid development of communication networks has provided a massive data analysis base, including social media, economic data, and historical event records, for social security event prediction based on data-driven approaches. The advent of data-driven approaches has revolutionized the prediction of these events, offering new theoretical insights and practical applications. Aiming at offering a systematic review of current data-driven prediction methods used in social security, this paper delves into the progress of this research from three novel perspectives, prediction factors, technical methods, and interpretability, and then analyzes future development trends. This paper contributes key insights into how social security event prediction can be improved and hopefully offers a comprehensive analysis that goes beyond the existing literature. |
| format | Article |
| id | doaj-art-9a413cff637f4108b1af44721bca7877 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9a413cff637f4108b1af44721bca78772025-01-24T13:19:54ZengMDPI AGApplied Sciences2076-34172025-01-0115258010.3390/app15020580Data-Driven Social Security Event Prediction: Principles, Methods, and TrendsNuo Xu0Zhuo Sun1School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSocial security event prediction can provide critical early warnings and support for public policies and crisis responses. The rapid development of communication networks has provided a massive data analysis base, including social media, economic data, and historical event records, for social security event prediction based on data-driven approaches. The advent of data-driven approaches has revolutionized the prediction of these events, offering new theoretical insights and practical applications. Aiming at offering a systematic review of current data-driven prediction methods used in social security, this paper delves into the progress of this research from three novel perspectives, prediction factors, technical methods, and interpretability, and then analyzes future development trends. This paper contributes key insights into how social security event prediction can be improved and hopefully offers a comprehensive analysis that goes beyond the existing literature.https://www.mdpi.com/2076-3417/15/2/580event predictionsocial securitydata driven |
| spellingShingle | Nuo Xu Zhuo Sun Data-Driven Social Security Event Prediction: Principles, Methods, and Trends Applied Sciences event prediction social security data driven |
| title | Data-Driven Social Security Event Prediction: Principles, Methods, and Trends |
| title_full | Data-Driven Social Security Event Prediction: Principles, Methods, and Trends |
| title_fullStr | Data-Driven Social Security Event Prediction: Principles, Methods, and Trends |
| title_full_unstemmed | Data-Driven Social Security Event Prediction: Principles, Methods, and Trends |
| title_short | Data-Driven Social Security Event Prediction: Principles, Methods, and Trends |
| title_sort | data driven social security event prediction principles methods and trends |
| topic | event prediction social security data driven |
| url | https://www.mdpi.com/2076-3417/15/2/580 |
| work_keys_str_mv | AT nuoxu datadrivensocialsecurityeventpredictionprinciplesmethodsandtrends AT zhuosun datadrivensocialsecurityeventpredictionprinciplesmethodsandtrends |