Big Data under the Microscope and Brains in Social Context: Integrating Methods from Computational Social Science and Neuroscience

被引:13
|
作者
O'Donnell, Matthew Brook [1 ]
Falk, Emily B. [1 ]
机构
[1] Univ Penn, Annenberg Sch Commun, Philadelphia, PA 19104 USA
关键词
fMRI; neuroscience; social network analysis; linguistic analysis; natural language processing; big data; computational social science; GOOGLE TRENDS; NETWORK; DYNAMICS; BEHAVIOR; THINKING; QUALITY; SMOKING;
D O I
10.1177/0002716215569446
中图分类号
D0 [政治学、政治理论];
学科分类号
0302 ; 030201 ;
摘要
Methods for analyzing neural and computational social science data are usually used by different types of scientists and generally seen as distinct, but they strongly complement one another. Computational social science methodologies can strengthen and contextualize individual-level analysis, specifically our understanding of the brain. Neuroscience can help to unpack the mechanisms that lead from micro- through meso- to macro-level observations. Integrating levels of analysis is essential to unified progress in social research. We present two example areas that illustrate this integration. First, combining egocentric social network data with neural variables from the egos provides insight about why and for whom certain types of antismoking messages may be more or less effective. Second, combining tools from natural language processing with neuroimaging reveals mechanisms involved in successful message propagation, and suggests links from microscopic to macroscopic scales.
引用
收藏
页码:274 / 289
页数:16
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