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基于动态网络的事件风险演变研究

闫志华, 唐锡晋   

  1. 中国科学院数学与系统科学研究院, 北京 100190
  • 收稿日期:2022-05-13 修回日期:2022-07-01 发布日期:2022-11-04
  • 基金资助:
    国家自然科学基金(71731002,71971190)资助课题.

闫志华, 唐锡晋. 基于动态网络的事件风险演变研究[J]. 系统科学与数学, 2022, 42(10): 2590-2601.

YAN Zhihua, TANG Xijin. Exploring the Transfer of Event Risk Based on Dynamic Networks[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(10): 2590-2601.

Exploring the Transfer of Event Risk Based on Dynamic Networks

YAN Zhihua, TANG Xijin   

  1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190
  • Received:2022-05-13 Revised:2022-07-01 Published:2022-11-04
为了从互联网媒体数据中识别风险事件,描述事件的演化结构和感知事件风险的演化规律,文章基于动态网络事件风险演变分析框架.文章构建时序动态网络表示事件的演化,使用Louvain算法识别事件,使用事件迁移概率构建事件之间的关系图.在识别事件演化结构的基础上,文章确定事件的主要演化路径,归纳出事件风险与事件生命周期之间的关系.研究结果表明,事件的演化存在着事件形成、事件合并和事件衰减等结构,事件演化结构够成了事件发展的主要路径,事件风险在事件生命周期的不同阶段存在差异.
In order to identify risk events from Internet media,describe the evolution structures of events and perceive the evolution patterns of event risk,this paper proposes an analysis framework of event risk evolution based on a dynamic network.We construct a time-series network to represent the dynamic development of events,use the Louvain algorithm to identify events,and employ the event transfer metric to construct relation graph between events.Based on the identification of event evolution structure,this paper identifies the main evolutionary paths of events and summarizes the relationship between event risk and event life cycle.The research results show that there are structures of event evolution such as event birth,event merge,and so on.The main paths of event evolution consist of evolution structure.Event risks vary at different stages of the event lifecycle.

MR(2010)主题分类: 

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[1] 屈晓妍.互联网使用与公众的社会风险感知.新闻与传播评论, 2011, 1:208-220.(Qu X Y. Internet usage and social risk perception of the public. Journalism&Communication Review, 2011, 1:208-220.)
[2] 李燕凌,孙龙,李诗悦,等.公共风险事件中网民风险感知的时空分布:来自H7N9的实证经验.情报杂志, 2020, 39(4):117-126.(Li Y L, Sun L, Li S Y, et al. Study on the temporal and spatial distribution of internet users'risk perception in public risk events:Empirical experience from H7N9. Journal of Intelligence, 2020, 39(4):117-126.)
[3] Allan J. Topic Detection and Tracking. Boston, MA:Springer, 2002.
[4] 闫志华,唐锡晋.融合高效用模式的在线媒体突发话题发现.系统工程理论与实践, 2021, 41(5):1138-1149.(Yan Z H, Tang X J. Bursty topic discovery of online media incorporating high utility pattern. Systems Engineering-Theory&Practice, 2021, 41(5):1138-1149.)
[5] Cai H, Huang Z, Srivastava D, et al. Indexing evolving events from tweet streams. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(11):3001-3015.
[6] 曹丽娜,唐锡晋.基于主题模型的BBS话题演化趋势分析.管理科学学报, 2014, 17(11):109-121.(Cao L N, Tang X J. Trends of BBS topics based on dynamic topic model. Journal of Management Sciences in China, 2014, 17(11):109-121.)
[7] Mao Q, Li X, Peng H, et al. Event prediction based on evolutionary event ontology knowledge. Future Generation Computer Systems, 2021,(115):76-89.
[8] Ozdikis O, Karagoz P, Oğuztüzün H. Incremental clustering with vector expansion for online event detection in microblogs. Social Network Analysis and Mining, 2017, 7(1):1-17.
[9] 刘晓娟,王晨琳.基于政务微博的信息公开与舆情演化研究-以新冠肺炎病例信息为例.情报理论与实践, 2021, 44(2):57-63.(Liu X J, Wang C L. Research on information disclosure and public opinion evolution based on government microblog:Taking case information of COVID-19 as an example. Information Studies:Theory&Application, 2021, 44(2):57-63.)
[10] Rule A, Cointet J P, Bearman P S. Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790-2014. Proceedings of the National Academy of Sciences, 2015, 112(35):10837-10844.
[11] Chen C, Ibekwe-SanJuan F, Hou J. The structure and dynamics of cocitation clusters:A multiple-perspective cocitation analysis. Journal of the American Society for Information Science and Technology, 2010, 61(7):1386-1409.
[12] Leydesdorff L, Nerghes A. Co-word maps and topic modeling:A comparison using small and medium-sized corpora (N<1,000). Journal of the Association for Information Science and Technology, 2017, 68(4):1024-1035.
[13] Blei D M, Lafferty J D. Dynamic topic models. The 23rd International Conference on Machine Learning, 2006, 113-120.
[14] Wang X, McCallum A. Topics over time:A non-Markov continuous-time model of topical trends. The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, 424-433.
[15] Du Y J, Yi Y T, Li X Y, et al. Extracting and tracking hot topics of micro-blogs based on improved Latent Dirichlet Allocation. Engineering Applications of Artificial Intelligence, 2020,(87):103279.
[16] Fu X, Li J, Yang K, et al. Dynamic online HDP model for discovering evolutionary topics from Chinese social texts. Neurocomputing, 2016,(171):412-424.
[17] Iwata T, Yamada T, Sakurai Y, et al. Online multiscale dynamic topic models. The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, 663-672.
[18] Keith N B F, Mitra T. Narrative maps:An algorithmic approach to represent and extract information narratives. Proceedings of the ACM on Human-Computer Interaction, 2021, 4(CSCW3):1-33.
[19] Xuan J, Luo X, Lu J, et al. Web event evolution trend prediction based on its computational social context. World Wide Web, 2020, 23(3):1861-1886.
[20] Liu Y, Peng H, Li J, et al. Event detection and evolution in multi-lingual social streams. Frontiers of Computer Science, 2020, 14(5):1-15.
[21] Koylu C. Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter. International Journal of Geographical Information Science, 2019, 33(4):805-832.
[22] Hu K, Luo Q, Qi K, et al. Understanding the topic evolution of scientific literatures like an evolving city:Using Google Word2Vec model and spatial autocorrelation analysis. Information Processing&Management, 2019, 56(4):1185-1203.
[23] 单晓红,庞世红,刘晓燕,等.基于事理图谱的网络舆情演化路径分析-以医疗舆情为例.情报理论与实践, 2019, 42(9):99-103.(Shan X H, Pang S H, Liu X Y, et al. Analysis on the evolution path of internet public opinions based on the event evolution graph:Taking medical public opinions as an example. Information Studies:Theory&Application, 2019, 42(9):99-103.)
[24] Slovic P E. The Perception of Risk. London:Earthscan Publications, 2000.
[25] 郑蕊.民众社会风险认知的影响因素及作用机制.博士论文.中国科学院心理研究所,北京, 2008.(Zheng R. The influence factors and mechanism of societal risk perception. Doctoral Dissertation. Institute of Psychology of the Chinese Academy of Sciences, Beijng, 2008.)
[26] 唐锡晋.两个定性综合集成支持技术.系统工程理论与实践, 2010, 30(9):1593-1606.(Tang X J. Two supporting technologies for qualitative meta-synthesis. Systems Engineering-Theory&Practice, 2010, 30(9):1593-1606.)
[27] Burns W J, Peters E, Slovic P. Risk perception and the economic crisis:A longitudinal study of the trajectory of perceived risk. Risk Analysis:An International Journal, 2012, 32(4):659-677.
[28] Tang X. Exploring on-line societal risk perception for harmonious society measurement. Journal of Systems Science and Systems Engineering, 2013, 22(4):469-486.
[29] 王炼,贾建民.突发性灾害事件风险感知的动态特征-来自网络搜索的证据.管理评论, 2014, 26(5):169-176.(Wang L, Jia J M. Risk perception dynamics in unexpected disaster events:Evidence from online search. Management Review, 2014, 26(5):169-176.)
[30] 贾玉改,唐锡晋.在线群体对社会风险事件关注焦点研究-以"纪念切尔诺贝利事件30周年"为例.系统科学与数学, 2017, 37(11):2178-2191.(Jia Y G, Tang X J. Exploring the focus of online community on societal risk event:The empirical analysis of online discussion on the 30th anniversary of Chernobyl disaster. Journal of Systems Science and Mathematical Sciences, 2017, 37(11):2178-2191.)
[31] 许诺,唐锡晋.基于百度热搜新闻词的社会风险事件5W提取研究.系统工程理论与实践, 2020, 40(2):334-342.(Xu N, Tang X J. A study on 5W extraction for societal risk events based on hot news search words. Systems Engineering-Theory&Practice, 2020, 40(2):334-342.)
[32] Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics:Theory and Experiment, 2008, 2008(10):10008.
[33] Newman M E J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 2006, 103(23):8577-8582.
[34] Liu J S, Ning K C, Chuang W C. Discovering and characterizing political elite cliques with evolutionary community detection. Social Network Analysis and Mining, 2013, 3(3):761-783.
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