地方政府危机治理政策传播与信息交互行为研究——基于大数据分析的视角
|
摘要
新媒体的迅速崛起与广泛应用,对地方政府危机治理能力与政策传播方式造成了挑战。论文运用个案分析法与社会网络分析法,对天津爆炸事故中的地方政府微博进行研究,发现不同类型危机信息传播具有不同的网络传播形态,其中灾后恢复政策传播存在信息提供者子群、信息转接桥梁子群和传播放大器子群不同类型的关键节点群体,三者在政策传播中发挥独特的作用。并认为,地方政府在官微发布和政策信息传播方面依然面临较大挑战和短板:地方政府官微总体来看网络影响力比较低;危机治理过程过分强调结果应对而忽视过程应对能力的建设;地方政府虚拟空间的危机治理政策信息发布和传播引导能力与线下政策制定与实务治理之间缺乏协同;虚拟空间传播网络三大子群的分化裂变进一步挑战地方政府的危机治理、网络发布与政策制定实施三位一体的整合能力。
The wide adoption of new media has challenged the ability of local crisis management. In this paper,we employ case study and social network analysis to analyze the micro-blog use of local government in Tianjin chemical explosion crisis. The results demonstrate that network structures of the policy and information propagation have different characteristics. Moreover, netizens can be categorized into three different subgroups,namely Critical feeders,Bridges,and Amplifiers,that essentially play very different roles in the policy propagation and government-netizen interaction process. In conclusion,local government still faces challenges in the policy propagation process.They have very low influence in the virtual world in general. The synchronization and coordination between virtual Interaction and policy making are still largely missing. At the same time, they are facing serious challenges presented by the emergence and maturity of three different online communities and their collaborative efforts in communication and negotiation with local governments.
The wide adoption of new media has challenged the ability of local crisis management. In this paper,we employ case study and social network analysis to analyze the micro-blog use of local government in Tianjin chemical explosion crisis. The results demonstrate that network structures of the policy and information propagation have different characteristics. Moreover, netizens can be categorized into three different subgroups,namely Critical feeders,Bridges,and Amplifiers,that essentially play very different roles in the policy propagation and government-netizen interaction process. In conclusion,local government still faces challenges in the policy propagation process.They have very low influence in the virtual world in general. The synchronization and coordination between virtual Interaction and policy making are still largely missing. At the same time, they are facing serious challenges presented by the emergence and maturity of three different online communities and their collaborative efforts in communication and negotiation with local governments.
引文
滨海发布(2015).情况通报·滨海在行动.新浪微博:http://weibo.com/3009788765Cy J1 CB6 KX?from=page_1001063009788765_profile&wvr=6&mod=weibotime&type=comment.2017年1月13日访问.
曹慧丹(2013).论社会化媒体的信息传播模式.新闻前哨,2013(4):33-35.
黄格(2015).复杂社会网络中舆情传播模型及影响因素分析.湘潭大学硕士学位论文.
刘淑华(2015).大数据时代网络抗争治理---基于江苏启东事件的个案研究.中国行政管理,7:121-125.
聂龑、吕涛、潘丽(2016).基于用户相对传播能力的分层网络传播模型---以“平江火电厂事件”为例.科技管理研究,13:164-169.
平安天津(2015).首次播发真实记录8.12保障事故救援现场.新浪微博:http://weibo.com3163782211/Cv Vo5 h Dvh?from=page_1001063163782211_profile&wvr=6&mod=weibotime&type=comment.2017年1月13日访问.
宋晓龙(2011).突发事件的互联网信息传播规律研究.哈尔滨工业大学硕士学位论文.
吴献举(2015).社会化媒体崛起背景下政府传播面临的挑战及对策.中州学刊,5:169-172.
西风残照汉陵阙(2015).微博评论.新浪微博:http://weibo.com/1644579953/Cy SI3ze7q from=page_1005051644579953_profile&wvr=6&mod=weibotime&type=comment.2017年1月13日访问.
夏宝君(2014).社会化媒体中的政治传播机制与策略研究.网友世界,21:111-111.
夏承遗、刘忠信、陈增强、袁著祉(2009).复杂网络上的传播动力学及其新进展.智能系统学报,4(5):392-397.
新华网(2016).天津港“8·12”爆炸事故调查告.新华网:http://www.tj.xinhuanet.com/2016-02/05/c_1118005221.htm.2016年6月26日访问.
钟伟军(2013).公共舆论危机中的地方政府微博回应与网络沟通---基于深圳“5·26飙车事件”的个案分析.公共管理学报,1:32-42.
张赛、徐恪、李海涛(2013).微博类社交网络中信息传播的测量与分析.西安交通大学学报,47(2):123-130.
周涛、柏文洁、汪秉宏、刘之景、严钢(2005).复杂网络研究概述.物理,34(1):31-36.
Berger,J.&Milkman,K.L.(2012).What Makes Online Content Viral?.Journal of Marketing Research,49(2):192-205.
Banos,R.A.,Borge-Holthoefer,J.&Moreno,Y.(2013).The Role of Hidden Influentials in the Diffusion of Online Information Cascades.EPJ Data Science,2(1):1-16.
Cao,N.,Lin,Y.R.,Sun,X.H.,Lazer,D.,Liu,S.X.&Qu,H.M.(2012).Whisper:Tracing the Spatiotemporal Process of Information Diffusion in Real Time.Transactions on Visualization&Computer Graphics,18:2649-2658.
Gibbons,D.E.(2007).Interorganizational Network Structures and Diffusion of Information through a Health System.American Journal of Public Health,97(9):1684-1692.
Hansen,L.K.,Arvidsson,A.,Nielsen,F.A.,Colleoni,E.&Etter,M.(2011).Good Friends,Bad News Affect and Virality in Twitter.Communications in Computer&Information Science,185:34-43.
Jalili,M.(2013).Effects of Leaders and Social Power on Opinion Formation in Complex Networks.Simulation:Transactions of the Society for Modeling and Simulation International,89(5):578-588.
Jiang,Y.C.(2014).Diffusion in Social Networks:A Multiagent Perspective.IEEE Transactions on Systems,45(2):198-213.
Kwak,H.,Lee,C.,Park,H.&Moon,S.(2010).What is Twitter,a Social Network or a News Media?.In Proceedings of the 19 th International Conference on World Wide Web.New York:ACMPublications.
Liu,Z.,Liu,L.&Li,H.(2012).Determinants of Information Retweeting in Microblogging.Internet Research,22(4):443-466.
Mishori,R.,Singh,L.O.&Levy,B.(2014).Mapping Physician Twitter Networks:Describing How They Work as a First Step in Understanding Connectivity,Information Flow,and Message Diffusion.Journal of Medical Internet Research,16(4):157-167.
Nematzadeh,A.,Ferrara,E.,Flammini,A.&Ahn,Y.Y.(2014).Optimal Network Modularity for Information Diffusion.Physical Review Letters,113(8):088701-1-088701-5.
Pastor-Satorras,R.&Vespignani,A.(2001).Epidemic Spreading in Scale Free Networks.Physical Review Letters,86:3200-3203.
Rogers,E.M.(2003).Diffusion of Innovations.New York:Free Press.
Starbird,K.&Palen,L.(2010).Pass it on Retweeting in Mass Emergency.Proceedings of the 7 th International ISCRAM Conference.Seattle,WA,1 May.
Tong,J.(2015).The Formation of an Agonistic Public Sphere:Emotions,the Internet and News Media in China.China Information,29(3):333-351.
Watts,D.J.&Strogatz,S.H.(1998).Collective Dynamics of“Small World”Networks.Nature,393:440-442.
Wyckhuys,K.A.G.&O'Neil,R.J.(2007).Role of Opinion Leadership,Social Connectedness and Information Sources in the Diffusion of IPM in Honduran Subsistence Maize Agriculture.International Journal of Fest Management,53(1):35-44.
Zafarani,R.,Abbasi,M.A.&Liu,H.(2014).Social Media Mining:An Introduction.Cambridge:Cambridge University Press.
(1)本文选取“滨海发布”“平安天津”“天津发布”和“天津消防”微博作为研究对象,有以下原因:一是四大官微代表不同行政级别和专业类型的政府微博,具有一定代表性,“滨海发布”和“天津发布”分别是事故发生地区政府和市政府微博,“平安天津”和“天津消防”微博是专业的政府机构微博;二是微博的样本量,四大官微在事故期间发布微博数量和微博转发评论数量颇具规模,影响广泛。
(2)本研究正式数据收集开始前首先进行了数据预收集,发现整个天津爆炸事故微博传播共有两个连续的传播高峰,第二个传播高峰的结束时间在2015年11月20日左右。
(1)详见新浪微博(平安天津,2015)。
(1)详见新浪微博(滨海发布,2015)。
(1)详见新浪微博(西风残照汉陵阙,2015)。
曹慧丹(2013).论社会化媒体的信息传播模式.新闻前哨,2013(4):33-35.
黄格(2015).复杂社会网络中舆情传播模型及影响因素分析.湘潭大学硕士学位论文.
刘淑华(2015).大数据时代网络抗争治理---基于江苏启东事件的个案研究.中国行政管理,7:121-125.
聂龑、吕涛、潘丽(2016).基于用户相对传播能力的分层网络传播模型---以“平江火电厂事件”为例.科技管理研究,13:164-169.
平安天津(2015).首次播发真实记录8.12保障事故救援现场.新浪微博:http://weibo.com3163782211/Cv Vo5 h Dvh?from=page_1001063163782211_profile&wvr=6&mod=weibotime&type=comment.2017年1月13日访问.
宋晓龙(2011).突发事件的互联网信息传播规律研究.哈尔滨工业大学硕士学位论文.
吴献举(2015).社会化媒体崛起背景下政府传播面临的挑战及对策.中州学刊,5:169-172.
西风残照汉陵阙(2015).微博评论.新浪微博:http://weibo.com/1644579953/Cy SI3ze7q from=page_1005051644579953_profile&wvr=6&mod=weibotime&type=comment.2017年1月13日访问.
夏宝君(2014).社会化媒体中的政治传播机制与策略研究.网友世界,21:111-111.
夏承遗、刘忠信、陈增强、袁著祉(2009).复杂网络上的传播动力学及其新进展.智能系统学报,4(5):392-397.
新华网(2016).天津港“8·12”爆炸事故调查告.新华网:http://www.tj.xinhuanet.com/2016-02/05/c_1118005221.htm.2016年6月26日访问.
钟伟军(2013).公共舆论危机中的地方政府微博回应与网络沟通---基于深圳“5·26飙车事件”的个案分析.公共管理学报,1:32-42.
张赛、徐恪、李海涛(2013).微博类社交网络中信息传播的测量与分析.西安交通大学学报,47(2):123-130.
周涛、柏文洁、汪秉宏、刘之景、严钢(2005).复杂网络研究概述.物理,34(1):31-36.
Berger,J.&Milkman,K.L.(2012).What Makes Online Content Viral?.Journal of Marketing Research,49(2):192-205.
Banos,R.A.,Borge-Holthoefer,J.&Moreno,Y.(2013).The Role of Hidden Influentials in the Diffusion of Online Information Cascades.EPJ Data Science,2(1):1-16.
Cao,N.,Lin,Y.R.,Sun,X.H.,Lazer,D.,Liu,S.X.&Qu,H.M.(2012).Whisper:Tracing the Spatiotemporal Process of Information Diffusion in Real Time.Transactions on Visualization&Computer Graphics,18:2649-2658.
Gibbons,D.E.(2007).Interorganizational Network Structures and Diffusion of Information through a Health System.American Journal of Public Health,97(9):1684-1692.
Hansen,L.K.,Arvidsson,A.,Nielsen,F.A.,Colleoni,E.&Etter,M.(2011).Good Friends,Bad News Affect and Virality in Twitter.Communications in Computer&Information Science,185:34-43.
Jalili,M.(2013).Effects of Leaders and Social Power on Opinion Formation in Complex Networks.Simulation:Transactions of the Society for Modeling and Simulation International,89(5):578-588.
Jiang,Y.C.(2014).Diffusion in Social Networks:A Multiagent Perspective.IEEE Transactions on Systems,45(2):198-213.
Kwak,H.,Lee,C.,Park,H.&Moon,S.(2010).What is Twitter,a Social Network or a News Media?.In Proceedings of the 19 th International Conference on World Wide Web.New York:ACMPublications.
Liu,Z.,Liu,L.&Li,H.(2012).Determinants of Information Retweeting in Microblogging.Internet Research,22(4):443-466.
Mishori,R.,Singh,L.O.&Levy,B.(2014).Mapping Physician Twitter Networks:Describing How They Work as a First Step in Understanding Connectivity,Information Flow,and Message Diffusion.Journal of Medical Internet Research,16(4):157-167.
Nematzadeh,A.,Ferrara,E.,Flammini,A.&Ahn,Y.Y.(2014).Optimal Network Modularity for Information Diffusion.Physical Review Letters,113(8):088701-1-088701-5.
Pastor-Satorras,R.&Vespignani,A.(2001).Epidemic Spreading in Scale Free Networks.Physical Review Letters,86:3200-3203.
Rogers,E.M.(2003).Diffusion of Innovations.New York:Free Press.
Starbird,K.&Palen,L.(2010).Pass it on Retweeting in Mass Emergency.Proceedings of the 7 th International ISCRAM Conference.Seattle,WA,1 May.
Tong,J.(2015).The Formation of an Agonistic Public Sphere:Emotions,the Internet and News Media in China.China Information,29(3):333-351.
Watts,D.J.&Strogatz,S.H.(1998).Collective Dynamics of“Small World”Networks.Nature,393:440-442.
Wyckhuys,K.A.G.&O'Neil,R.J.(2007).Role of Opinion Leadership,Social Connectedness and Information Sources in the Diffusion of IPM in Honduran Subsistence Maize Agriculture.International Journal of Fest Management,53(1):35-44.
Zafarani,R.,Abbasi,M.A.&Liu,H.(2014).Social Media Mining:An Introduction.Cambridge:Cambridge University Press.
(1)本文选取“滨海发布”“平安天津”“天津发布”和“天津消防”微博作为研究对象,有以下原因:一是四大官微代表不同行政级别和专业类型的政府微博,具有一定代表性,“滨海发布”和“天津发布”分别是事故发生地区政府和市政府微博,“平安天津”和“天津消防”微博是专业的政府机构微博;二是微博的样本量,四大官微在事故期间发布微博数量和微博转发评论数量颇具规模,影响广泛。
(2)本研究正式数据收集开始前首先进行了数据预收集,发现整个天津爆炸事故微博传播共有两个连续的传播高峰,第二个传播高峰的结束时间在2015年11月20日左右。
(1)详见新浪微博(平安天津,2015)。
(1)详见新浪微博(滨海发布,2015)。
(1)详见新浪微博(西风残照汉陵阙,2015)。