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Multimodal Analysis and Prediction of Latent User Dimensions

机译：潜在用户维度的多模式分析和预测

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Humans upload over 1.8 billion digital images to the internet each day, yet the relationship between the images that a person shares with others and his/her psychological characteristics remains poorly understood. In the current research, we analyze the relationship between images, captions, and the latent demographic/psychological dimensions of personality and gender. We consider a wide range of automatically extracted visual and textual features of images/captions that are shared by a large sample of individuals (N ≈ 1,350). Using correlational methods, we identify several visual and textual properties that show strong relationships with individual differences between participants. Additionally, we explore the task of predicting user attributes using a multi-modal approach that simultaneously leverages images and their captions. Results from these experiments suggest that images alone have significant predictive power and, additionally, multimodal methods outperform both visual features and textual features in isolation when attempting to predict individual differences.
机译：人类每天在互联网上上传超过18亿张数字图像，但是人们与他人共享的图像与其心理特征之间的关系仍然知之甚少。在当前的研究中，我们分析图像，标题以及人格和性别的潜在人口统计/心理维度之间的关系。我们考虑了由大量个体（N≈1,350）共享的图像/字幕自动提取的视觉和文字特征。使用相关方法，我们确定了几种视觉和文本属性，这些属性显示了参与者之间个体差异的密切关系。此外，我们探索了使用多模式方法来预测用户属性的任务，该方法同时利用了图像及其标题。这些实验的结果表明，单独的图像具有显着的预测能力，此外，在尝试预测个体差异时，多模式方法在视觉特征和文本特征方面均优于单独的表现。

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