互联网集体行动何以可能?——基于网络大数据的研究框架建构
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摘要
互联网集体行动已经成为一种崭新的集体行动形式,给全球政治格局带来诸多不稳定因素。目前互联网集体行动领域的研究尤其是实证研究困难重重,需要建立更为完善和成熟的互联网集体行动研究框架。互联网集体行动从微观到宏观的理想动员结构为"舆论动员(公共性形成)—公众参与—集体行动"。在互联网集体行动大数据采集方面,考虑到互联网集体行动的实践过程,可能的大数据来源是根据互联网集体行动的发展过程确定事件过程的关键时点,因此应采集关键时点截面的社交网络数据。在互联网集体行动结构变迁的测量方面,结合互联网集体行动事件的实践过程,主要关注社交网络结构、时序扩散网络的测量。
Internet collective action has become a new form of collective action,bringing many unstable factors to the global political landscape.At present,there are many difficulties in the field of Internet collective action research,especially in empirical research.It is necessary to establish a more complete and mature Internet collective action research framework.The ideal mobilization structure of Internet collective action from micro to macro is "mobilization of public opinion(formation of publicity)-public participation-collective action".We determine the critical time points of the event process based on the development process of the collective action of the Internet,and collect the social network data of the key time points through the big data collection method.In the measurement of changes in the collective action structure of the Internet,combined with the practice of Internet collective action events,we mainly focus on the measurement of social network structure and time-series diffusion network.
Internet collective action has become a new form of collective action,bringing many unstable factors to the global political landscape.At present,there are many difficulties in the field of Internet collective action research,especially in empirical research.It is necessary to establish a more complete and mature Internet collective action research framework.The ideal mobilization structure of Internet collective action from micro to macro is "mobilization of public opinion(formation of publicity)-public participation-collective action".We determine the critical time points of the event process based on the development process of the collective action of the Internet,and collect the social network data of the key time points through the big data collection method.In the measurement of changes in the collective action structure of the Internet,combined with the practice of Internet collective action events,we mainly focus on the measurement of social network structure and time-series diffusion network.
引文
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[4]Gary King,Jennifer Pan,Margaret E.Roberts.How Censorship in China Allows Government Criticism but Silences Collect ive Expression[J].American Polit ical Science Review,2013,(2).
[5]曾繁旭,黄广生.网络意见领袖社区的构成、联动及其政策影响:以微博为例[J].开放时代,2012,(4).
[6]陆学艺.当代中国社会阶层研究报告[M].北京:社会科学文献出版社,2002.
[7]桂勇,李秀玫,郑雯,等.网络极端情绪人群的类型及其政治与社会意涵[J].党政视野,2015,(10).
[8]马得勇,王丽娜.中国网民的意识形态立场及其形成一个实证的分析[J].社会,2015,(5).
[9]刘能.怨恨解释、动员结构和理性选择---有关中国都市地区集体行动发生可能性的分析[J].开放时代,2004,(4).
[10]Ithiel de Sola Pool,Manfred Kochen.Contacts and Influence[J].Social Networks,1978,(1).
[11]吴信东,李毅,李磊.在线社交网络影响力分析[J].计算机学报,2014,(4).
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[14]Francois Lorrain,Harrison C.White.Structural Equivalence of Individuals in Social Networks[J].Social Networks,1977,(1).
[15]Noah E.Friedkin,Eugene C.Johnsen.Social Influence and Opinions[J].Journal of Mathematical Sociology,1990,(3-4).
[16]Sinan Aral,Dylan Walker.Identifying Influential and Susceptible Members of Social Networks[J].Science,2012,(6092).
[17]Gordon C.Winston.Subsidies,Hierarchy and Peers:The Awkward Economics of Higher Education[J].Journal of Economic Perspectives,1999,(1).
[18]Walter Quattrociocchi,Antonio Scala,Cass R.Sunstein.Echo Chambers on Facebook[J].SSRN Electronic Journal,2016,(1).
[19]Eytan Bakshy,Solomon Messing,Lada A.Adamic.Exposure to Ideologically Diverse News and Opinion on F acebook[J].Science,2015,(6239).
[20]李振鹏,唐锡晋.集体行动的阈值模型[J].系统科学与数学,2014,(5).
[21]Michael Conover,Jacob Ratkiewicz,Matthew Francisco,Bruno Goncalves.Political Polarization on Twitter[C].International Conference on Weblogs&Social Media,Barcelona,California,Spain,July 17-21,2011.
[22]Jan Lorenz,Heiko Rauhut,Frank Schweitzer,Dirk Helbing.How Social Influence can Undermine the Wisdom of Crowd Effect[J].Proceedings of t he Nat ional Academy of Sciences of t he Unit ed St at es of America,2011,(22).
[23]Mark Granovetter.Threshold Models of Collective Behavior[J].American Journal of Sociology,1978,(6).
[24]Jaewon Yang,Jure Leskovec.Patterns of Temporal Variation in Online Media[C].Proceedings of the Fourth In ter na tional Conference on Web Search and Web Data Mining,Hongkong,Chi na,February 9-12,2011.
[25]Eytan Bakshy,Jake M.Hofman,Winter Mason,Duncan J.Watts.Everyone’s an Influencer:Quantifying Influnce on Twitter[C].Proceedings of the Fourth International Conference on Web Search and Web Data Mining,Hongkong,Chi na,February 9-12,2011.
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