Computer-based personality judgments are more accurate than those made by humans

Abstract

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.personality judgment | social media | computational social science | artificial intelligence | big data

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

“…Van de Ven et al [25] based their analyses on LinkedIn, a job-related social media platform, yet they did not find strong correlations between personality traits and user profiles, except for Extraversion. YouYou et al [26] demonstrated that computer-based judgments about an individual can be more accurate than those made by friends, spouse, and even the individual himself, if sufficient data is available.…”

From Face Images and Attributes to Attributes

“…Youyou et al [7] show that Facebook 'likes' can be used to predict the scores of BIG5 personality traits. Note that in this case the number of items/attributes that can be 'liked' is very large, while the 'likes' are very sparse and binary.…”

“…Facebook BIG5 personality traits dataset was recently introduced by Youyou et al [7] for studying the prediction of personality traits from the 'likes' history of the users of the Facebook social network. A 'like' shows a positive association of a user with online and offline items/subjects such as sport, musicians, products, celebrities, etc.…”

“…Furthermore, we can use attributes to predict other attributes and thus to solve the attribute completion task (see Figure 1). Note that with most of our settings we are capable to achieve state-of-the-art performance for most attributes of two recent large-scale datasets: CelebA [6] and Facebook BIG5 [7].…”

“…Attribute prediction is a challenging problem and while for biometrics the research spans over decades, the other attributes have been investigated less and, moreover, most of the recent research treats each attribute individually as a prediction from face image domain to the attribute label. The recent release of large-scale attribute datasets such as CelebA [6] (with face attributes) and Facebook BIG5 (with personality traits and 'likes') [7] fueled the recent advances and the interest resurgence for attributes as research topic. Convolutional Neural Networks (CNNs) proved their effectiveness on many computer vision tasks such as classification and end-to-end mappings [8][9][10].…”