| 面向公共安全风险防控的疫情网络舆情预警研究——以刚果埃博拉病毒为例 |
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| 引用本文： | 袁媛.面向公共安全风险防控的疫情网络舆情预警研究——以刚果埃博拉病毒为例[J].情报科学,2022,39(1):44-50. |
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| 作者姓名： | 袁媛 |
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| 作者单位： | 中国刑事警察学院禁毒与治安学院 |
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| 基金项目： | 国家重点研发计划“公共安全风险防控与应急技术装备”重点专项(2017YFC0821400);中国刑事警察学院中央高校基本科研业务费重大项目培育计划项目“普遍安全观视域下世界典型国家社会安全指数及其指标体系构建研究”(D2020059)。 |
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| 摘 要： | 【目的/意义】网络舆情预警作为反映社会舆情的“晴雨表”和“提示器”，有助于政府部门通过公告、沟通、情
绪安慰和教育活动对社会进行科学管理。【方法/过程】本文基于Python数据爬虫技术，将刚果（金）赤道省疫情期间
的 38天分为 38个时间点，进一步构造 SVM 模型，并用 Matlab对 SVM 模型进行训练。其中 5月 18日至 6月 18日数
据为训练样本，6月19日至6月25日数据为检验样本。【结果/结论】通过实证研究，危机程度大于和小于0.5的情况
均合理有效，预警模型实用性强，对政府、社会、媒体应对危机产生了较大价值。【创新/局限】但由于本研究仅是针
对埃博拉展开，从Twitter中获取的数据量有限，因此存在一定研究局限性。未来将尝试选择基于更多的主题，从多
个来源提取更多数据，以对网络舆情危机预警机制进行更加系统、全面地研究。
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| 关 键 词： | 机器学习 PYTHON 网络舆情 埃博拉疫情 预警 |
The Internet Public Opinion Early Warning of Epidemics for Public Security Risk Prevention and Control——Taking Congo Ebola as an Example |
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| YUAN Yuan.The Internet Public Opinion Early Warning of Epidemics for Public Security Risk Prevention and Control——Taking Congo Ebola as an Example[J].Information Science,2022,39(1):44-50. |
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| Authors: | YUAN Yuan |
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| Institution: | (School of Narcotics Control and Public Order Studies,Criminal Investigation Police University of China,Shenyang 110854,China) |
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| Abstract: | 【Purpose/significance】Network public opinion early warning.as a‘barometer’and‘reminder’to reflect social public opin?
ion.is helpful for government departments to manage society scientifically through announcement.communication.emotional comfort and educational activities【. Method/process】Based on Python data crawler technology.38 days during the epidemic in Equatoria Prov?ince of Congo (Kinshasa) were divided into 38 time points.and the SVM model was further constructed.and the SVM model was trained with Matlab.The training samples were from May 18 to June 18 and the test samples were from June 19 to June 25【. Result/conclusion】
Through empirical research.the situation in which the degree of crisis is greater than or less than 0.5 is reasonable and effective.and the early warning model is practical.which has great value for the government.society and media to deal with the crisis【. Innovation/lim?itation】However.since this article is only for the Ebola epidemic.the amount of data obtained from Twitter is limited.so there are cer?tain research limitations.In the future.we will try to extract more data from multiple sources based on more topics to conduct a more sys?tematic and comprehensive research on the online public opinion crisis early warning mechanism. |
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| Keywords: | machine learning Python network public opinion Ebola epidemic early warning |
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