突发事件信息传播网络中的关键节点动态识别研究
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摘要
为有效识别突发事件信息传播在不同阶段中的关键节点及其演化特征,本文结合危机传播的生命周期,提出一种考虑用户行为特征、网络全局信息以及影响力衰退机制的关键节点动态识别方法。以"哈维"飓风事件为案例进行研究,利用Spearman相关分析和SIR传播模型检验了方法的合理性,并在不同演化阶段关键节点特征对比分析的基础上,提出针对不同阶段突发事件信息传播的舆情治理策略。实验结果表明:与PageRank方法相比,该方法识别出的关键节点在传播速度和传播范围方面表现出一定的优势;随着信息传播不同阶段的演化,关键节点的认证率呈现上升趋势,信息优势表现为先下降后上升,响应优势呈现出相反趋势,而结构优势差异并不显著;在突发事件舆情治理方面,可重点识别潜伏期中高原创、高信息优势和非认证的关键节点,注意搜集爆发期中普通型关键节点掌握的信息,强化蔓延期中各类型关键节点之间的协同,留意消散期中小范围群体的聚集现象。
In order to effectively identify the key nodes in information propagation networks during emergencies and their dynamic characteristics during different stages of an emergency, this paper proposes a method that introduces crisis lifecycle theory and considers characteristics of user behaviors and global network attributes in the information cparosep atog actioonnd uocft stohcei aelx npeetriwmoerknts., Sasp ewarelml aans st hceo rdreelcaatyi olna wan oalf ysspisr eaanddi ntgh ei nSfIluRe nmcoed. eHl uwrreircea unsee dH taor vveeyr ifwya st hceh eofsfeenc tiavs ean esstsu doyf this method. The results show that the TPR method is somewhat better than PageRank in terms of spreading speed and spreading scope. With the evolution of different stages of information propagation, the verification rate of key nodes increases. Therefore, the information advantage decreases at first, then increases after the chronic period, while the response advantage shows an opposite trend. There are no significant differences in the aspect of structural advantage. The results shed light on the management of public opinion: administrators should a) focus on the key nodes in the prodromal period that are non-verified and outstanding in terms of originality and information advantage; b) pay more attention to information provided by key nodes that are common netizens in the breakout period; c) strengthen the coordination among different types of key nodes in the chronic period; and d) keep an eye on small-scale clusters during the recovery period.
In order to effectively identify the key nodes in information propagation networks during emergencies and their dynamic characteristics during different stages of an emergency, this paper proposes a method that introduces crisis lifecycle theory and considers characteristics of user behaviors and global network attributes in the information cparosep atog actioonnd uocft stohcei aelx npeetriwmoerknts., Sasp ewarelml aans st hceo rdreelcaatyi olna wan oalf ysspisr eaanddi ntgh ei nSfIluRe nmcoed. eHl uwrreircea unsee dH taor vveeyr ifwya st hceh eofsfeenc tiavs ean esstsu doyf this method. The results show that the TPR method is somewhat better than PageRank in terms of spreading speed and spreading scope. With the evolution of different stages of information propagation, the verification rate of key nodes increases. Therefore, the information advantage decreases at first, then increases after the chronic period, while the response advantage shows an opposite trend. There are no significant differences in the aspect of structural advantage. The results shed light on the management of public opinion: administrators should a) focus on the key nodes in the prodromal period that are non-verified and outstanding in terms of originality and information advantage; b) pay more attention to information provided by key nodes that are common netizens in the breakout period; c) strengthen the coordination among different types of key nodes in the chronic period; and d) keep an eye on small-scale clusters during the recovery period.
引文
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[10]谭雪晗,涂艳,马哲坤.基于SNA的事故灾难舆情关键用户识别及治理[J].情报学报,2017,36(3):297-306.
[11]Simon T,Goldberg A,Adini B.Socializing in emergencies-A review of the use of social media in emergency situations[J].International Journal of Information Management,2015,35(5):609-619.
[12]Oh O,Kwon K K H K,Rao H R.An exploration of social media in extreme events:rumor theory and twitter during the Haiti earth‐quake[C]//Proceedings of the International Conference on Infor‐mation Systems,Saint Louis,Missouri,USA,2010:1-13.
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[14]Chen D B,LüL Y,Shang M S,et al.Identifying influential nodes in complex networks[J].Physica A,2012,391(4):1777-1787.
[15]任卓明,邵凤,刘建国,等.基于度与集聚系数的网络节点重要性度量方法研究[J].物理学报,2013,62(12):522-526.
[16]Bonacich P.Factoring and weighting approaches to status scores and clique identification[J].Journal of mathematical sociology,1972,2(1):113-120.
[17]Freeman L C.Centrality in social networks conceptual clarifica‐tion[J].Social networks,1978,1(3):215-239.
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[23]Hou B N,Yao Y P,Liao D S.Identifying all-around nodes for spreading dynamics in complex networks[J].Physica A,2012,391(15):4012-4017.
[24]Weng J S,Lim E P,Jiang J,et al.TwitterRank:finding topic-sen‐sitive influential twitters[C]//Proceedings of the 3rd ACM Inter‐national Conference on Web Search and Data Mining.New York:ACM Press,2010:261-270.
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[26]Li Q,Zhou T,LüL Y,et al.Identifying influential spreaders by weighted LeaderRank[J].Physica A,2013,404(24):47-55.
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[28]Yao C Y,He G M.Key nodes analysis of microblogging public opinion spread about public emergencies[C]//Proceedings of the International Conference on Modeling,Simulation and Applied Mathematics.Thailand:Atlantis Press,2015:264-268.
[29]Fink S.Crisis management:Planning for the inevitable[M].New York:American Management Association,1986:5-6.
[30]Zhang Z K,Liu C,Zhan X X,et al.Dynamics of information dif‐fusion and its applications on complex networks[J].Physics Re‐ports,2016,651:1-34.
[31]Yang J,Counts S.Predicting the speed,scale,and range of infor‐mation diffusion in Twitter[C]//Proceedings of 4th International AAAI Conference on Weblogs and Social Media,Washington DC,2010:355-358.
[32]Riquelme F,González-Cantergiani P.Measuring user influence on Twitter:A survey[J].Information Processing&Management,2016,52(5):949-975.
[33]张鹏威,刘红丽,俞丽娟,等.微博信息传播中的用户影响力研究综述[J].情报科学,2016,34(8):160-164.
[34]Mei Y,Zhong Y L,Yang J.Finding and analyzing principal fea‐tures for measuring user influence on Twitter[C]//Proceedings of the 2015 IEEE First International Conference on Big Data Com‐puting Service and Applications.San Francisco,CA,USA,2015:478-486.
[35]Leavitt A,Burchard E,Fisher D,et al.The influentials:New ap‐proaches for analyzing influence on Twitter[J].Web Ecology,2009,4(2):1-18.
[36]Brin S,Page L.The anatomy of a large-scale hypertextual web search engine[J].Computer Networks and ISDN Systems,1998,30(1):107-117.
[37]Huang J M,Li C,Wang W Q,et al.Temporal scaling in informa‐tion propagation[J].Scientific Reports,2014,4:1-6.
[38]Yang J,Leskovec J.Modeling information diffusion in implicit networks[C]//Proceedings of the 2010 IEEE 10th International Conference on Data Mining.Sydney,NSW,Australia,2010:599-608.
[39]Phillips M E.Hurricane harvey Twitter dataset[EB/OL].[2017-11-22].https://digital.library.unt.edu/ark:/67531/metadc993940/.
[40]方滨兴,许进,李建华,等.在线社交网络分析[M].北京:电子工业出版社,2014:24-26.
[41]王晨旭,晓宏,秦涛,等.微博消息传播中意见领袖影响力建模研究[J].软件学报,2015,26(6):1473-1485.
[42]姜启源,谢金星,叶俊.数学模型[M].第3版.北京:高等教育出版社,2003:138-140.
[43]张玥,孙霄凌,浦正宁,等.微博舆情传播影响因素研究--基于信源特征和信息形式的视角[J].情报资料工作,2014(3):59-64.
[44]江燕青,许鑫.半衰期视角的微博信息老化研究--以高校官方微博为例[J].图书情报知识,2016(2):92-100.
[45]Lerman K,Ghosh R.Information contagion:an empirical study of the spread of news on digg and Twitter social networks[C]//Proceedings of 4th International AAAI Conference on Weblogs and Social Media.Washington DC:AAAI,2010:166-176.
[2]曹学艳,段飞飞,方宽,等.网络论坛视角下突发事件舆情的关键节点识别及分类研究[J].图书情报工作,2014,58(4):65-70.
[3]武澎,王恒山,李煜.突发事件信息传播超网络中枢纽节点的判定研究[J].管理评论,2013,25(6):104-111.
[4]刘志明,刘鲁.微博网络舆情中的意见领袖识别及分析[J].系统工程,2011,29(6):8-16.
[5]戴丽娜.微博舆论领袖的识别方法与管理策略研究[J].新闻记者,2012(9):26-29.
[6]Cha M,Haddadi H,Benevenuto F,et al.Measuring User Influ‐ence in Twitter:The Million Follower Fallacy[C]//Proceedings of the 4th International AAAI Conference on Weblogs and Social Media,Washington DC,2010:10-17.
[7]Ding Z Y,Jia Y,Zhou B,et al.Measuring the spreadability of us‐ers in microblogs[J].Journal of Zhejiang University-Science C,2013,14(9):701-710.
[8]贾亚敏,安璐,李纲.城市突发事件网络信息传播时序变化规律研究[J].情报杂志,2015,34(4):91-96.
[9]Li L F,Zhang Q P,Tian J,et al.Characterizing information propa‐gation patterns in emergencies:A case study with Yiliang Earth‐quake[J].International Journal of Information Management,2018,38(1):34-41.
[10]谭雪晗,涂艳,马哲坤.基于SNA的事故灾难舆情关键用户识别及治理[J].情报学报,2017,36(3):297-306.
[11]Simon T,Goldberg A,Adini B.Socializing in emergencies-A review of the use of social media in emergency situations[J].International Journal of Information Management,2015,35(5):609-619.
[12]Oh O,Kwon K K H K,Rao H R.An exploration of social media in extreme events:rumor theory and twitter during the Haiti earth‐quake[C]//Proceedings of the International Conference on Infor‐mation Systems,Saint Louis,Missouri,USA,2010:1-13.
[13]Albert R,Jeong H,Barabasi A L.Error and attack tolerance of complex networks[J].Nature,2000,406(6794):378-382.
[14]Chen D B,LüL Y,Shang M S,et al.Identifying influential nodes in complex networks[J].Physica A,2012,391(4):1777-1787.
[15]任卓明,邵凤,刘建国,等.基于度与集聚系数的网络节点重要性度量方法研究[J].物理学报,2013,62(12):522-526.
[16]Bonacich P.Factoring and weighting approaches to status scores and clique identification[J].Journal of mathematical sociology,1972,2(1):113-120.
[17]Freeman L C.Centrality in social networks conceptual clarifica‐tion[J].Social networks,1978,1(3):215-239.
[18]Freeman L C.A set of measures of centrality based on between‐ness[J].Sociometry,1977,40(1):35-41.
[19]Poulin R,Boily M C,Masse B R.Dynamical systems to define centrality in social networks[J].Social Networks,2000,22(3):187-220.
[20]Comin C H,Costa L F.Identifying the starting point of a spread‐ing process in complex networks[J].Physical Review E,2011,84(5):1-6.
[21]Kitsak M,Gallos L K,Havlin S S,et al.Identification of influen‐tial spreaders in complex networks[J].Nature Physics,2010,6(11):888-893.
[22]Zeng A,Zhang C J.Ranking spreaders by decomposing complex networks[J].Physics Letters A,2013,377(14):1031-1035.
[23]Hou B N,Yao Y P,Liao D S.Identifying all-around nodes for spreading dynamics in complex networks[J].Physica A,2012,391(15):4012-4017.
[24]Weng J S,Lim E P,Jiang J,et al.TwitterRank:finding topic-sen‐sitive influential twitters[C]//Proceedings of the 3rd ACM Inter‐national Conference on Web Search and Data Mining.New York:ACM Press,2010:261-270.
[25]Zhang J,Ackerman M S,Adamic L.Expertise networks in online communities:structure and algorithms[C]//Proceedings of the16th International Conference on World Wide Web,Banff,Alber‐ta,Canada,2007:221-230.
[26]Li Q,Zhou T,LüL Y,et al.Identifying influential spreaders by weighted LeaderRank[J].Physica A,2013,404(24):47-55.
[27]Tyshchuk Y,Wallace W A.Actionable Information during Ex‐treme Events[C]//Proceedings of the 2012 ASE/IEEE Internation‐al Conference on Privacy,Security,Risk and Trust and 2012 Inter‐national Confernece on Social Computing.Amsterdam,Nether‐lands,2012:338-347.
[28]Yao C Y,He G M.Key nodes analysis of microblogging public opinion spread about public emergencies[C]//Proceedings of the International Conference on Modeling,Simulation and Applied Mathematics.Thailand:Atlantis Press,2015:264-268.
[29]Fink S.Crisis management:Planning for the inevitable[M].New York:American Management Association,1986:5-6.
[30]Zhang Z K,Liu C,Zhan X X,et al.Dynamics of information dif‐fusion and its applications on complex networks[J].Physics Re‐ports,2016,651:1-34.
[31]Yang J,Counts S.Predicting the speed,scale,and range of infor‐mation diffusion in Twitter[C]//Proceedings of 4th International AAAI Conference on Weblogs and Social Media,Washington DC,2010:355-358.
[32]Riquelme F,González-Cantergiani P.Measuring user influence on Twitter:A survey[J].Information Processing&Management,2016,52(5):949-975.
[33]张鹏威,刘红丽,俞丽娟,等.微博信息传播中的用户影响力研究综述[J].情报科学,2016,34(8):160-164.
[34]Mei Y,Zhong Y L,Yang J.Finding and analyzing principal fea‐tures for measuring user influence on Twitter[C]//Proceedings of the 2015 IEEE First International Conference on Big Data Com‐puting Service and Applications.San Francisco,CA,USA,2015:478-486.
[35]Leavitt A,Burchard E,Fisher D,et al.The influentials:New ap‐proaches for analyzing influence on Twitter[J].Web Ecology,2009,4(2):1-18.
[36]Brin S,Page L.The anatomy of a large-scale hypertextual web search engine[J].Computer Networks and ISDN Systems,1998,30(1):107-117.
[37]Huang J M,Li C,Wang W Q,et al.Temporal scaling in informa‐tion propagation[J].Scientific Reports,2014,4:1-6.
[38]Yang J,Leskovec J.Modeling information diffusion in implicit networks[C]//Proceedings of the 2010 IEEE 10th International Conference on Data Mining.Sydney,NSW,Australia,2010:599-608.
[39]Phillips M E.Hurricane harvey Twitter dataset[EB/OL].[2017-11-22].https://digital.library.unt.edu/ark:/67531/metadc993940/.
[40]方滨兴,许进,李建华,等.在线社交网络分析[M].北京:电子工业出版社,2014:24-26.
[41]王晨旭,晓宏,秦涛,等.微博消息传播中意见领袖影响力建模研究[J].软件学报,2015,26(6):1473-1485.
[42]姜启源,谢金星,叶俊.数学模型[M].第3版.北京:高等教育出版社,2003:138-140.
[43]张玥,孙霄凌,浦正宁,等.微博舆情传播影响因素研究--基于信源特征和信息形式的视角[J].情报资料工作,2014(3):59-64.
[44]江燕青,许鑫.半衰期视角的微博信息老化研究--以高校官方微博为例[J].图书情报知识,2016(2):92-100.
[45]Lerman K,Ghosh R.Information contagion:an empirical study of the spread of news on digg and Twitter social networks[C]//Proceedings of 4th International AAAI Conference on Weblogs and Social Media.Washington DC:AAAI,2010:166-176.
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