ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2023, Vol. 31 ›› Issue (3): 317-329.doi: 10.3724/SP.J.1042.2023.00317

• 研究方法 •    下一篇

基于大数据的文化心理分析

吴胜涛1,2(), 茅云云1, 吴舒涵2, 冯健仁3, 张庆鹏3, 谢天4, 陈浩5,6, 朱廷劭7   

  1. 1厦门大学社会与人类学院
    2厦门大学传播研究所, 厦门 361005
    3广州大学公共管理学院, 广州510006
    4武汉大学哲学学院, 武汉 430072
    5南开大学周恩来政府管理学院, 天津 300350
    6中山大学广州粤港澳社会心理建设研究中心, 广州 510006
    7中国科学院心理研究所, 北京 100101
  • 收稿日期:2022-01-14 出版日期:2023-03-15 发布日期:2022-12-22
  • 通讯作者: 吴胜涛 E-mail:michaelstwu@xmu.edu.cn;wust2011@163.com
  • 基金资助:
    国家社会科学基金重点项目(17AZD041);一般项目(20BSH111);及厦门大学校长基金(20720221017)

The big data analysis in cultural psychology

WU Michael Shengtao1,2(), MAO Yunyun1, WU Shuhan2, FENG Jianren3, ZHANG Qingpeng3, XIE Tian4, CHEN Hao5,6, ZHU Tingshao7   

  1. 1School of Sociology and Anthropology, Xiamen University, Xiamen 361005, China
    2Institute of Communication, Xiamen University, Xiamen 361005, China
    3Public Administration School, Guangzhou University, Guangzhou 510006, China
    4School of Philosophy, Wuhan University, Wuhan 430072, China
    5Zhou Enlai School of Government, Nankai University, Tianjin 300350, China
    6Guangzhou Guangdong-Hong Kong-Macao Greater Bay Area Research Center for Social Psychological, Sun Yat-sen University, Guangzhou 510006, China
    7Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2022-01-14 Online:2023-03-15 Published:2022-12-22
  • Contact: WU Michael Shengtao E-mail:michaelstwu@xmu.edu.cn;wust2011@163.com

摘要:

随着大数据技术与文化心理学的融合发展, 计算文化心理学作为一门新兴交叉学科逐渐兴起, 大尺度、近乎全样本的文化心理分析真正得以实现。计算文化心理学关注的文化变量主要围绕个人主义/集体主义这一文化心理学使用最为广泛的维度展开, 分析方法包括特征词典、机器学习、社会网络分析、仿真模拟等, 分析思路包括时间维度上的文化变迁效应以及空间维度上的文化地理效应。当然, 计算文化心理学在为传统文化心理研究提供新方法、新范式的同时, 也存在解码失真、样本偏差、词语多义性、隐私风险等局限, 未来研究应重视变量理论解释、文化动态演化分析、学科深度整合、生态效度等问题。

关键词: 大数据, 文化, 计算文化心理学, 文化变迁, 文化地理

Abstract:

With the further development of computers and big data technology, human society and its cultural forms are undergoing profound changes. The production and interaction of cultural symbols have become increasingly complex, and cultural members and their social networks have left numerous texts and behavior footprints, which makes it necessary to describe, predict, and even change the culture, so that computable cultural symbols and their interaction process have gradually become the research object of cultural psychology. In this vein, Computational Cultural Psychology (CCP), which employs big data and computation tools to understand cultural symbols and their interaction processes, has emerges rapidly, making large-scale or even full sample cultural analysis possible. The key variables of CCP are mainly about individualism and collectivism, and the analysis technologies include feature dictionaries, machine learning, social networks analysis, and simulation.
New research avenues of CCP involve the cultural change effect from the temporal perspective and cultural geography effect from the spatial perspective. For the former, Google Ngram Viewer, Google News, Google Search, name archives, pop songs, and micro-blogs were used to analyze the cultural changes after the long-term historical development and the short-term economic transformation. For the latter, both social media (e.g., Twitter, Facebook, and Weibo) and large-scale survey were used to analyze the cultural differences of various countries or regions in different geographic spaces, as well as the relationship between culture and environment, such as cultural diversity along the "Belt and Road", person - environment fit and cultural value mismatch across different regions in a country or all over the world.
It should be noted that there are several limitations in CCP, including decoding distortion, sample bias, semasiological variation, and privacy risk, although new methods and paradigms are provided. In future directions, theoretical interpretation of variables, cultural dynamics, interdisciplinary integration, and ecological validity should be seriously concerned. In particular, accurate definition and theoretical interpretation of big data measurement are needed; a variety of big data corpus (e.g., historical archives) should be used for the evolutionary analysis of dynamic cultures; deep integration, but not conflict, should be encouraged between culture psychology and the sciences of computer, communication, and history; and the "scenarios" of big data should be considered in promoting the ecological validity of cultural psychology.
Taken together, a review of the emergence of CCP, as well as the empirical research on the big data analysis of cultural change and cultural geography, is helpful in understanding the advantages, limitations, and future direction of this new field, which sheds light on theoretical and methodological innovation of cultural psychology.

Key words: big data, culture, computational cultural psychology, cultural change, cultural geography

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