[目的/意义] 利用事理图谱研究重大突发事件网络舆情诱发与缓释机理有助于赋能舆情引导监管，保障治理活动有据可查、有章可循。 [方法/过程] 设计重大突发事件描述方法，经舆情泛化等步骤构建事理图谱模型，将模型中的标记要素作为前因条件变量赋值后得到抽象化组态并进行三维动因分析和驱动传导路径提取，据此阐明诱发与缓释机理。 [结果/结论] 结果显示，事理图谱模型能够有效解析重大突发事件网络舆情诱发与缓释机理，动因分析与驱动传导路径表征有利于突破静态分析壁垒、提升纵向解析深度。根据模型开发图谱自动化生成与抽象化构造的 EPL 系统可以用于实证并简化操作流程。不足在于，仅针对诱发与缓释机理展开研究，未深入探讨次生舆情等类似现象，期待今后提供更全面研究。
[Purpose/Significance] Using event evolutionary graphs to study the mechanisms that trigger and mitigate network public opinion in major emergencies can help empower public opinion guidance and supervision, ensuring that governance activities are well-documented and rule-based. [Method/Process] This paper designed the description method of major emergencies, constructed the event evolutionary graph model through the steps of public opinion generalization, assigned the marker elements in the model as antecedent conditional variables, obtained abstraction configurations, and conducted three-dimensional motivation analysis and drive transmission path extraction, thereby clarifying the mechanism of induction and mitigation. [Result/Conclusion] The results show that the event evolutionary graph model can effectively analyze the mechanism of triggering and mitigating network public opinion in major emergencies, and the analysis of the motivation and driving conduction paths can be beneficial for overcoming static analysis barriers and enhancing the depth of vertical analysis. According to the model, an EPL system with automatic generation of atlas and abstraction structure is developed, which can be used for empirical analysis and simplify the operation process. The drawback is that this article only focuses on the mechanisms of induction and release, without delving into similar phenomena such as secondary public opinion. Looking forward to providing more comprehensive research in the future.
[1] 王晰巍,王小天,李玥琪.重大突发事件网络舆情UGC的事理图谱构建研究——以自然灾害7·20河南暴雨为例[J].图书情报工作, 2022, 66(16):13-23.
[2] 澎湃新闻.关于建立健全"情指勤舆"一体化实战化运行机制——推进公安执法规范化建设的实践和思考[EB/OL].[2023-04-02]. https://www.thepaper.cn/newsDetail_forward_15004744.
[3] MBA智库.舆情[EB/OL].[2023-04-02]. https://wiki.mbalib.com/wiki/舆情.
[4] 王连喜.网络舆情领域相关概念分布及其关系辨析[J].现代情报, 2019, 39(6):132-141.
[5] 马腾,殷跃,赵树宽,等.多维数据融合的突发公共卫生事件网络舆情演化特征研究[J].情报理论与实践, 2022, 45(12):170-177.
[6] 盛振江.突发事件网络舆情公安应急对策研究[J].山东警察学院学报, 2019, 31(1):144-150.
[7] 杨红.应急管理视角下做好网络舆情应对工作的四个抓手——基于北京市应急管理舆情工作实践的思考[J].科技传播, 2022, 14(10):90-92, 102.
[8] 田婧倩,刘晓星.媒体舆情、政府监管与市场行为——基于信息博弈的结构性视角[J].计量经济学报, 2021, 1(1):141-160.
[9] 王会权,谢东方,刘璐.基于大数据思维与方法的网络舆情监控研究[J].中国广播电视学刊, 2019(2):86-91.
[10] 李昊青,兰月新,侯晓娜,等.网络舆情管理的理论基础研究[J].现代情报, 2015, 35(5):25-29, 40.
[11] 刘放桐.现代西方哲学[M].北京:人民出版社, 1990:36-48.
[12] 刘毅.略论网络舆情的概念、特点、表达与传播[J].理论界, 2007(1):11-12.
[13] 王兰成.多学科视域网络舆情知识图谱研究的现状和展望[J].情报学报, 2020, 39(10):1104-1113.
[14] 曾子明,李青青,孙守强,等.面向突发公共卫生事件网络舆情的事理图谱构建及演化分析[J/OL].情报理论与实践[2023-04-02]. http://kns.cnki.net/kcms/detail/11.1762.G3.20230320.1524.004.html.
[15] 单晓红,庞世红,刘晓燕,等.基于事理图谱的网络舆情事件预测方法研究[J].情报理论与实践, 2020, 43(10):165-170, 156.
[16] 夏立新,陈健瑶,余华娟.基于事理图谱的多维特征网络舆情事件可视化摘要生成研究[J].情报理论与实践, 2020, 43(10):157-164.
[17] DENG S, RANGWALA H, NING Y. Learning dynamic context graphs for predicting social events[C]//Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery&data mining, anchorage. New York:Association for Computing Machinery, 2019:1007-1016.
[18] WEN Z, FANG Y. Trend:temporal event and node dynamics for graph representation learning[C]//Proceedings of the ACM Web conference 2022. Lyon:Association for Computing Machinery, 2022:1159-1169.
[19] WU L, CHEN Y, SHEN K, et al. Graph neural networks for natural language processing:a survey[J]. Foundations and trends® in machine learning, 2023, 16(2):119-328.
[20] 刘嘉毅,陈玉萍,华丽.旅游网络舆情诱发机制与影响效应研究——基于雪乡宰客事件新浪微博评论的内容分析[J].信息系统学报, 2019(1):55-67.
[21] 杨绘荣,范雅钧.从刺激到反应:网络舆情的生成及疏导之策[J].行政与法, 2022(8):22-32.
[22] 王超.我国突发性网络舆情事件的关联网络结构分析[J].现代情报, 2019, 39(12):121-130.
[23] 崔鹏,张巍,何毅,等.突发公共事件网络舆情演化及政府应对能力研究[J].现代情报, 2018, 38(2):75-83, 95.
[24] ZHAO J, HE H, ZHAO X, et al. Modeling and simulation of microblog-based public health emergency-associated public opinion communication[J]. Information processing&management, 2022, 59(2):102846.
[25] YU L, CHEN H, LUO W, et al. Online public opinion evaluation through the functional resonance analysis method and deep analysis[J]. Plos one, 2021, 16(12):e0261009.
[26] LIU L, TU Y, ZHOU X. How local outbreak of COVID-19 affect the risk of internet public opinion:a Chinese social media case study[J]. Technology in society, 2022, 71:102113.
[27] 吴沿沿.商业银行经营效率及影响因素分析[D].上海:华东师范大学, 2012.
[28] 胡吉亚.战略性新兴产业信贷融资的现状、问题与对策——基于"海萨尼转换"博弈分析[J].软科学, 2021, 35(2):1-6.
[29] 李永壮,叶春凤.微时代校园网络舆情分析及应对机制——以衡水学院调查数据为样本[J].衡水学院学报, 2018, 20(1):98-100, 104.
[30] 孔德鹏,郎玫,史传林.信息内卷化:政府网络舆情治理能力的提升路径[J].情报杂志, 2022, 41(11):120-128.
[31] 黄仕靖,吴川徽,袁勤俭,等.基于情感分析的突发公共卫生事件舆情时空演化差异研究[J].情报科学, 2022, 40(6):149-159.
[32] 任凯,郭黎黎,张燕.社交网络舆情事件的生命周期模型研究[J].科技传播, 2019, 11(23):1-3.
[33] 沙灜,梁棋,王斌.中文变体词的识别与规范化综述[J].信息安全学报, 2016, 1(3):77-87.
