摘要
大规模地实时计算民众的心理特征有利于维护社会的和谐稳定,但传统的心理测验方法不能有效地满足上述需求.本研究提出了基于网络数据分析的心理计算方法,通过分析用户的"微博"数据建立心理计算模型(心理健康状态计算模型与主观幸福感计算模型),据此来计算民众的心理特征.研究结果显示,心理计算模型的建模效果良好,而建模方法与建模目标是影响建模效果的两个重要因素.本研究表明,可以通过分析网络数据来计算民众的心理特征,此方法有利于改善心理测验的实施规模与施测效率.
To improve social harmony and stability, it is essential to acquire public psychological profiles in real time. However, traditional methods of psychological assessment have failed to meet the requirement. This paper proposes a novel method for predicting psychological features based on web behavioral data. Using a microblogging platform, we built predicting models for identifying mental health status and subjective well-being. The correlation between the predicted and actual values of depression can reach 0.41, and the highest correlation on subjective well-being is 0.6. The results indicate an effective overall performance of the established predicting models. This study demonstrates that, based on web data analysis, it is possible to efficiently predict psychological features and to update the predicted outcomes in real time.
出处
《科学通报》
EI
CAS
CSCD
北大核心
2015年第11期994-1001,共8页
Chinese Science Bulletin
基金
国家高技术研究发展计划(AA01A606)
国家重点基础研究发展计划(CB744600)
中国科学院重点部署项目(KJZD-EW-L04)
中国科学院战略性先导科技专项(XDA06030800)资助
关键词
网络数据
心理计算
心理健康状态
主观幸福感
web data, psychological computing, mental health status, subjective well-being
