全数据模式的幻象与网络大数据的代表性
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摘要
大数据时代为计算社会科学的发展提供了契机。有一种观点认为,由于大数据是"样本=总体",因此它不存在采样偏差和数据代表性问题。虽然大数据驱动下的社会科学研究取得诸多成果,但也有不少失败的案例,对这些案例进行分析可见,"总体数据"是相对于具体的研究对象和研究问题而言的,大数据时代并不能保证社会科学开展全数据模式研究。数字鸿沟、用户偏好等客观存在的问题,使网络大数据往往是用户自我选择样本。在很多情况下,"全数据模式"只是缺乏深思明辨而勾勒出的一幅幻象,社会科学研究者应对此具备清醒的认识,方能作出高质量的研究。
The era of big data provides opportunity to the development of computational social science. There is a view that given "everything can be digitized",social science can acquire research-required "whole data",as "big data is whole data",sampling bias and data representativeness issue no longer exist. Although big-data-driven social scientific research has made a series of achievements,there are also certain unsuccessful cases,through which it can be found that "whole data" is relative to the specific research object and issue,the era of big data cannot guarantee whole data research model. Digital divide,users' preferences and other objective problems make online big data mostly user self-selected sample. In many cases,"whole data model" is an illusion created by lacking of care discernment,social science researchers should have a clear understanding of this,so that they can conduct high quality research.
The era of big data provides opportunity to the development of computational social science. There is a view that given "everything can be digitized",social science can acquire research-required "whole data",as "big data is whole data",sampling bias and data representativeness issue no longer exist. Although big-data-driven social scientific research has made a series of achievements,there are also certain unsuccessful cases,through which it can be found that "whole data" is relative to the specific research object and issue,the era of big data cannot guarantee whole data research model. Digital divide,users' preferences and other objective problems make online big data mostly user self-selected sample. In many cases,"whole data model" is an illusion created by lacking of care discernment,social science researchers should have a clear understanding of this,so that they can conduct high quality research.
引文
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(1)必应是微软搜索引擎的名称。
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[6]Ipsos Mori.“Remain”in EU Still Ahead Although Lead Has Narrowed[EB/OL].https://www.ipsos.com/ipsos-mori/enuk/remain-eu-still-ahead-although-lead-has-narrowed,2016-07-28.
[7]W Jennings,S Fisher.Expert Predictions of the 2016 EU Referendum[EB/OL].https://www.psa.ac.uk/sites/default/files/PSA%20EU2016%20Report.pdf,2016-06-27.
[8]陈晓平.大数据预测英国公投:将以4%的微弱优势选择留欧[EB/OL].http://www.sohu.com/a/85596456_202972,2016-08-20.
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[10]Zillien N,Hargittai E.Digital Distinction:Status-Specific Types of Internet Usage[J].Social Science Quarterly,2009(2).
[11]Dave Chaffey.Global Social Media Research Summary 2017[EB/OL].http://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/,2016-03-27.
[12]中国互联网络信息中心.第41次中国互联网络发展状况统计报告[EB/OL].http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201803/P020180305409870339136.pdf,2018-06-01.
[13]Dimaggio P,Hargittai E.From the“Digital Divide”to“Digital Inequality”:Studying Internet Use as Penetration Increases[J].Current Opinion in Obstetrics&Gynecology,2001(1).
[14]Bonfadelli H.The Internet and Knowledge Gaps:A Theoretical and Empirical Investigation[J].European Journal of Communication,2002(1).
[15]奇智睿思.2015微信用户数据报告:已覆盖中国90%以上的智能手机[EB/OL].http://news.ittime.com.cn/news/news_4840.shtml,2017-10-01.
[16]企鹅智酷.2017微信用户&生态研究报告[EB/OL].http://tech.qq.com/a/20170424/004233.htm#p=1,2017-10-01.
[17]Hargittai E.Is Bigger Always Better?Potential Biases of Big Data Derived from Social Network Sites[J].Annals of the AmericanAcademy of Political&Social Science,2015(1).
[18]Kate Crawford.Following You:Disciplines of Listening in Social Media[J].Continuum,2009(4).
[19]Stephens M,Poorthuis A.Follow thy Neighbor:Connecting the Social and the Spatial Networks on Twitter[J].Computers Environment&Urban Systems,2015(3).
[20]Ruths D,Pfeffer J.Social Sciences.Social Media for Large Studies of Behavior[J].Science,2014(6213).
(1)必应是微软搜索引擎的名称。