Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

被引:63
作者
Halford, Susan [1 ,2 ]
Savage, Mike [3 ,4 ]
机构
[1] Univ Southampton, Sociol, Southampton, Hants, England
[2] Univ Southampton, Web Sci Inst, Southampton, Hants, England
[3] London Sch Econ, Sociol, London, England
[4] London Sch Econ, Int Inequal Inst, London, England
来源
SOCIOLOGY-THE JOURNAL OF THE BRITISH SOCIOLOGICAL ASSOCIATION | 2017年 / 51卷 / 06期
关键词
big data; computational methods; sociology; symphonic social science; visualisation; CHALLENGES; CRISIS;
D O I
10.1177/0038038517698639
中图分类号
C91 [社会学];
学科分类号
030301 ; 1204 ;
摘要
Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about the social', and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label symphonic social science'. This bears both striking similarities and significant differences to the big data paradigm and - as such - offers the potential to do big data analytics differently. This offers value to those already working with big data - for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent - and to sociologists, offering a mode of practice that might shape big data analytics for the future.
引用
收藏
页码:1132 / 1148
页数:17
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