Private traits and attributes are predictable from digital records of human behavior
Private traits and attributes are predictable from digital records of human behavior
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DOI：
10.1073/pnas.1218772110
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发表时间：
2013-04-09
影响因子：
11.1
通讯作者：
Graepel, Thore
中科院分区：
文献类型：
--
作者：
Kosinski, Michal;Stillwell, David;Graepel, Thore
我们表明，易于获取的数字行为记录，即Facebook的“喜欢”，可以用来自动准确地预测一系列高度敏感的个人属性，包括：性取向、种族、宗教和政治观点、人格特征、智力、幸福、成瘾物质的使用、父母分居、年龄和性别。该分析基于5.8万多名志愿者的数据集，这些志愿者提供了他们在Facebook上的点赞、详细的人口统计资料以及几项心理测试的结果。该模型使用降维方法对点赞数据进行预处理，然后将这些数据输入逻辑/线性回归中，以预测个人的点赞心理人口学特征。该模型在88%的案例中正确区分了同性恋和异性恋男性，在95%的案例中正确区分了非裔美国人和白种人，在85%的案例中正确区分了民主党人和共和党人。对于人格特质“开放性”，预测准确度接近标准人格测验的重测准确度。我们给出了属性和喜欢之间关联的例子，并讨论了在线个性化和隐私的含义。
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.