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Computational Personality Prediction Based on Digital Footprint of A Social Media User

机译：基于社交媒体用户的数字足迹的计算人格预测

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The digitisation process of objects and operations of the real world is quite active, i.e. creating their digital entities or models. People regularly leave enough of their data in social networks services and on various sites, thus forming their unique digital footprint. Based on the obtained digital footprint, it is possible to create a complete digital entity of a person in the hyperspace of social media. However, a person is a complex system; therefore, the model of a digital entity must be multi-scale. In this paper, the relationship of such a weakly formalizable side of the user, such as his psychometric indicators, and his digital footprint in the social network sites have been studied. First, it was conducted a series of experiments on the prediction of psychometric, based on data from social networks (Facebook and Vkontakte), during which two prediction approaches were compared: multi-response forecasting, when all psychometrics are predicted simultaneously, and univariate models for each personality trait. Then, in the course of comparing results from different social networks, an analysis was conducted to determine whether any psychometric could correlate equally well in different media environments. For this purpose, a correlation matrix was constructed between the features and psychometrics. Then, due to the small sample size of one of the datasets, an experiment was conducted showing changes in the quality of predictive models, when the initial sample is expanded by adding data from another dataset that has a similar distribution. That is, the possibility of cross-media learning was investigated.
机译：真实世界的对象和操作的数字化过程非常活跃，即创建数字实体或模型。人们经常在社交网络服务和各种网站上留出足够的数据，从而形成他们独特的数字占地面积。基于所获得的数字足迹，可以在社交媒体的高度中创建一个人的完整数字实体。但是，一个人是一个复杂的系统;因此，数字实体的模型必须是多尺度的。在本文中，已经研究了用户的这种弱可形式尺寸的关系，例如他的心理学指标，以及社交网站中的数字足迹。首先，基于来自社交网络（Facebook和VKontakte）的数据，在比较了两种预测方法的数据上进行了一系列关于心理学的预测的实验：多响应预测，当同时预测所有精神仪和单变量模型时对于每个人格特质。然后，在比较来自不同社交网络的结果的过程中，进行了分析以确定不同媒体环境中是否具有同样好的情况。为此目的，在特征和精神仪之间构建相关矩阵。然后，由于其中一个数据集的样本量小，进行了实验，示出了通过从具有类似分布的另一个数据集的数据扩展初始样本时，显示了预测模型的质量的变化。也就是说，调查了跨媒体学习的可能性。

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