“社会5.0”时代的大数据风险治理
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摘要
随着"社会5.0"时代的来临,社会风险出现了复杂化、多元化及动态化等新特点,使风险治理面临前所未有的挑战。大数据为其提供了全新的战略资源和技术支撑。大数据具有风险预警预控复合工具集、追踪社会风险轨迹、人群智慧和数据共享等功能,促使传统风险治理向智慧化、非正式、预警云和可视化等模式转变。不仅能增强治理资源的互联性及有效性,而且使风险治理具有跨部门智能治理、"多对多"治理、事先预防和实时分析等光明前景。同时强调大数据也存在应对隐私泄露、噪音和信任等陷阱。为了充分发挥大数据风险治理的巨大潜在优势,政策制定者不仅需要先进人才与技术支持,还需要转变传统治理思维,才能使政策真正对社会风险治理认知及行为产生深刻影响。
With the advent of"Society 5.0"era, social risk has appeared new characteristics such as being complicated, being diversified, and being dynamic, leading to the result that risk governance faces unprecedented challenges. Big data provides it with brand-new strategic resources and technical support. Big data has the functions of risk pre-warning and pre-control"compound tool set", tracking"social risk trajectory","crowd intelligence", and"data sharing", which promotes mode transformation from traditional risk governance to the mode of"intelligence", of"informality", of"early warning cloud", and of"visualization". It can not only enhance the inter-connection and effectiveness of governance resources, but also makes risk governance has the bright prospect of cross-departmental intelligent governance, of"many-to-many"governance, of beforehand precaution, and of real-time analysis. It also highlights that big data also has pitfalls in dealing with privacy leaks, noise, and trust. To realize the huge potential advantages of risk governance with big data, policy maker not only needs advanced talents and technical support, but still needs to change traditional governance thinking. Only in this way can make policy produces profound effect to knowledge of social risk governance and its behavior truly.
With the advent of"Society 5.0"era, social risk has appeared new characteristics such as being complicated, being diversified, and being dynamic, leading to the result that risk governance faces unprecedented challenges. Big data provides it with brand-new strategic resources and technical support. Big data has the functions of risk pre-warning and pre-control"compound tool set", tracking"social risk trajectory","crowd intelligence", and"data sharing", which promotes mode transformation from traditional risk governance to the mode of"intelligence", of"informality", of"early warning cloud", and of"visualization". It can not only enhance the inter-connection and effectiveness of governance resources, but also makes risk governance has the bright prospect of cross-departmental intelligent governance, of"many-to-many"governance, of beforehand precaution, and of real-time analysis. It also highlights that big data also has pitfalls in dealing with privacy leaks, noise, and trust. To realize the huge potential advantages of risk governance with big data, policy maker not only needs advanced talents and technical support, but still needs to change traditional governance thinking. Only in this way can make policy produces profound effect to knowledge of social risk governance and its behavior truly.
引文
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[18]张乐,童星.重大决策社会稳定风险评估路径的优化:公众参与环节的再思考[J].广州大学学报(社会科学版),2016(10):18-27.
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[20] ASAKO M. et al, Expression of negative emotional responses to the 2011 great east Japan earthquake:analysis of big data from social media[J].Japanese journal of psychology, 2015,86(2):102-111.
[2] ONORATIT, DIAZP. Giving meaning to tweets in emergency situations:a semantic approach for filtering and visualizing social data[J]. Springerplus, 2016, 5(1):1782.
[3]刘英基.大数据时代的社会冲突创新治理研究[J].中国特色社会主义研究,2016(1):84-88.
[4]周利敏,龙智光.大数据时代的灾害预警创新——以阳江预售信息发布中心为案例[J].武汉大学学报,2017(3):121-131.
[5] CHOY YC,SOO I. K, BAE. Yong-Geun, e-Gov's big data utilization plan for social crisis management[J].Journal of the Korea Institute of Information and communication engineering,2017, 21(2):435-442.
[6] CRAMPTON J.W.Collect it all:national security, big data and governance[J].GeoJournal,2015(80):519–531.
[7] BENJAMIN, B et al. Cybercrime:attack motivations and implications for big data and national security[M]//BABAK,A(eds.)et al.Application of big data for national security:a practitioners guide to emerging technologies.Oxford:Butterworth Heinemann(Elsevier), 2015:108-129.
[8] SEON HWA CHOI.The model for sensing of disaster signs based on big data[J].Journal of the Korean society of hazard mitigation,2014,14(2):149-157.
[9] REUTER,C, et al. Big data in a crisis? Creating social media datasets for crisis management research[J]. Published online,2017, 15(3):251-261.
[10] TIONG T. G,PEI-CHEN SUN.Teaching social media analytics:an assessment based on natural disaster postings[J]. Journal of information systems education,2015,26(1):27-36.
[11] LIANGM, et al. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data[J].Acta Tropica, 2016,162(2):188-195+8.
[12] BOGOMOLOV A.et al.Moves on the street:classifying crime hotspots using aggregated anonymized data on people dynamics[J].Big Data,2015,3(3):148-158.
[13] STEVE V T.How big data, the internet of things and social networks can change security[J]. Security Solutions for enterprise security leaders,2014,51(3):44-46.
[14] SEANG-TAE K. Next generation e-government strategies and asks for the smart society-based on Korea'scase[J].E-Governance,2013,36(1):12-24.
[15] JING X,et al. A sensitive object-oriented approach to big surveillance data compression for social security applications in smart cities[J].Software practice&experience,2016,47(8):1061-1080.
[16]周利敏.迈向大数据时代的城市风险治理——基于多案例的研究[J].西南民族大学学报,2016(5):91-98.
[17] FITZHUGH, S M et al.Spatio-temporal filtering techniques for the detection of disaster-related communication[J]. Social science research,2016(59):137-154.
[18]张乐,童星.重大决策社会稳定风险评估路径的优化:公众参与环节的再思考[J].广州大学学报(社会科学版),2016(10):18-27.
[19]周利敏.灾害管理:国际前沿及理论综述[J].云南社会科学,2018(5):18-26.
[20] ASAKO M. et al, Expression of negative emotional responses to the 2011 great east Japan earthquake:analysis of big data from social media[J].Japanese journal of psychology, 2015,86(2):102-111.