Advances in Psychological Science ›› 2023, Vol. 31 ›› Issue (3): 317-329.doi: 10.3724/SP.J.1042.2023.00317
• Research Method • Next Articles
WU Michael Shengtao1,2(), MAO Yunyun1, WU Shuhan2, FENG Jianren3, ZHANG Qingpeng3, XIE Tian4, CHEN Hao5,6, ZHU Tingshao7
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
CLC Number:
WU Michael Shengtao, MAO Yunyun, WU Shuhan, FENG Jianren, ZHANG Qingpeng, XIE Tian, CHEN Hao, ZHU Tingshao. The big data analysis in cultural psychology[J]. Advances in Psychological Science, 2023, 31(3): 317-329.
文化心理变量/研究问题 | 计算方法 | 操作过程 | 主要发现 |
---|---|---|---|
个人主义/集体主义的变迁 | 基于特征词典的频次分析 | 个人主义为第一人称单数(如I, me, my, mine, myself)或行为特征词使用频率(如get, choose); 集体主义为第一人称复数(如we, us, our, ours, ourselves)或行为特征词使用频率(如give, obliged) (Greenfield, | Google 电子图书语料库显示, 过去200年、特别是二战以后, 世界范围内出现了个人主义文化增强、集体主义文化式微(或稳定)的历史变化趋势。 |
社会网络分析 | 集体主义为微博用户@他人的次数(Wu, Li et al., | 2010~2016年间, 短暂的经济增长放缓伴随集体主义的显著增长。集体主义文化(如中国)下 成群行走者比例高于个人主义文化(如澳大利亚)。 | |
个人主义、文化松紧度与情绪表达规范 | 基于特征词典的频次分析 | 基于表情符号的Unicode标注, 把emoji表情转译成文本, 然后再用LIWC对每个表情符号进行积极情绪、消极情绪计算和排序(Lu et al., | 个人主义国家或地区的用户更倾向于使用正面的表情符号, 而较少使用负面的表情符号; 来自松文化地区的人更可能表达积极的情绪, 而不太可能表达消极的情绪。 |
个人主义与个性解放/自我表达的地区差异 | 机器学习/人格预测模型 | 基于Twitter个人主义指标(第一人称单数使用率)与已有国家个性解放/自我表达得分(自治、平等、选择与言论自由)建立回归模型, 然后用其他国家地区的个人主义指标预测其个性解放/自我表达得分(吴胜涛 等, | 个人主义正向预测自治、平等, 即:个人主义越强的国家或地区, 越倾向于自治、平等的价值观。 |
性别/种族平等、个人主义生活/工作态度的变迁 | 机器学习/词嵌入联想测验 | 基于Google News & Books数据集的词向量或词嵌入文件, 以靶词和属性词的语义关联(余弦相似度)作为态度指标, 分析其时间变化趋势 (Garg et al., | 1910~1990年间, 性别偏见和种族偏见随着时间的推移而减少。1950~1990年间, 个人主义/集体主义与工作、成就词的关联下降。 |
不确定规避、个人主义与社交距离和创新扩散 | 社会仿真模拟 | 社交网络距离为连接每两个主体之间最短路径的平均值; 创新扩散为由外部随机决定的t = 0处的特定位置(Desmarchelier & Fang, | 不确定规避对创新扩散速率有负向影响, 而个人主义对扩散速率有正向影响。 |
文化心理变量/研究问题 | 计算方法 | 操作过程 | 主要发现 |
---|---|---|---|
个人主义/集体主义的变迁 | 基于特征词典的频次分析 | 个人主义为第一人称单数(如I, me, my, mine, myself)或行为特征词使用频率(如get, choose); 集体主义为第一人称复数(如we, us, our, ours, ourselves)或行为特征词使用频率(如give, obliged) (Greenfield, | Google 电子图书语料库显示, 过去200年、特别是二战以后, 世界范围内出现了个人主义文化增强、集体主义文化式微(或稳定)的历史变化趋势。 |
社会网络分析 | 集体主义为微博用户@他人的次数(Wu, Li et al., | 2010~2016年间, 短暂的经济增长放缓伴随集体主义的显著增长。集体主义文化(如中国)下 成群行走者比例高于个人主义文化(如澳大利亚)。 | |
个人主义、文化松紧度与情绪表达规范 | 基于特征词典的频次分析 | 基于表情符号的Unicode标注, 把emoji表情转译成文本, 然后再用LIWC对每个表情符号进行积极情绪、消极情绪计算和排序(Lu et al., | 个人主义国家或地区的用户更倾向于使用正面的表情符号, 而较少使用负面的表情符号; 来自松文化地区的人更可能表达积极的情绪, 而不太可能表达消极的情绪。 |
个人主义与个性解放/自我表达的地区差异 | 机器学习/人格预测模型 | 基于Twitter个人主义指标(第一人称单数使用率)与已有国家个性解放/自我表达得分(自治、平等、选择与言论自由)建立回归模型, 然后用其他国家地区的个人主义指标预测其个性解放/自我表达得分(吴胜涛 等, | 个人主义正向预测自治、平等, 即:个人主义越强的国家或地区, 越倾向于自治、平等的价值观。 |
性别/种族平等、个人主义生活/工作态度的变迁 | 机器学习/词嵌入联想测验 | 基于Google News & Books数据集的词向量或词嵌入文件, 以靶词和属性词的语义关联(余弦相似度)作为态度指标, 分析其时间变化趋势 (Garg et al., | 1910~1990年间, 性别偏见和种族偏见随着时间的推移而减少。1950~1990年间, 个人主义/集体主义与工作、成就词的关联下降。 |
不确定规避、个人主义与社交距离和创新扩散 | 社会仿真模拟 | 社交网络距离为连接每两个主体之间最短路径的平均值; 创新扩散为由外部随机决定的t = 0处的特定位置(Desmarchelier & Fang, | 不确定规避对创新扩散速率有负向影响, 而个人主义对扩散速率有正向影响。 |
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