Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.

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Predicting personality from patterns of behavior collected with smartphones.

＂There＇s all kinds of people that need that kind of help, and I don＇t think anybody is better at providing it than GLIDE.＂ ——”股神”沃伦·巴菲特（Warren Buffet）是美国亿万富翁全球知名的投资商．他的慈善午餐拍卖（Charity Lunch Auction）每年都会吸引世人的目光。近几年．该拍卖活动的成交价格一直都维持在150万美元以上，去年更是创了历史最高纪录。

Sense and Nonsense

We argue that it is useful to distinguish between three key goals of personality science—description, prediction and explanation—and that attaining them often requires different priorities and meth...

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits:

The increasing availability of high–dimensional, fine–grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter ...

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Personality Research and Assessment in the Era of Machine Learning

State-of-the-art personality prediction with text data mostly relies on bottom up, automated feature generation as part of the deep learning process. More traditional models rely on hand-crafted, theory-based text-feature categories. We propose a novel deep learning-based model which integrates traditional psycholinguistic features with language model embeddings to predict personality from the Essays dataset for Big-Five and Kaggle dataset for MBTI. With this approach we achieve state-of-the-art model performance. Additionally, we use interpretable machine learning to visualize and quantify the impact of various language features in the respective personality prediction models. We conclude with a discussion on the potential this work has for computational modeling and psychological science alike.11Code: https://github.com/yashsmehta/personality-prediction

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Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To judge the quality of a clustering result, different cluster validation procedures have been proposed in the literature. While there is extensive work on classical validation techniques, such as internal and external validation, less attention has been given to validating and replicating a clustering result using a validation dataset. Such a dataset may be part of the original dataset, which is separated before analysis begins, or it could be an independently collected dataset. We present a systematic structured framework for validating clustering results on validation data that includes most existing validation approaches. In particular, we review classical validation techniques such as internal and external validation, stability analysis, hypothesis testing, and visual validation, and show how they can be interpreted in terms of our framework. We precisely define and formalise different types of validation of clustering results on a validation dataset and explain how each type can be implemented in practice. Furthermore, we give examples of how clustering studies from the applied literature that used a validation dataset can be classified into the framework.

Validation of cluster analysis results on validation data: A systematic framework

/pdf/behavioral-patterns-in-smartphone-usage-predict-big-five-2s4aqv0ygn.pdf

Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits

Repository: Predicting Personality from Patterns of Behavior Collected with Smartphones

Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming and unreproducible manual trial-and-error process to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods, e.g., based on resampling error estimation for supervised machine learning, can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.

Theresa Ullmann

Papers

Predicting personality from patterns of behavior collected with smartphones.

Validation of cluster analysis results on validation data: A systematic framework

Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits

Repository: Predicting Personality from Patterns of Behavior Collected with Smartphones

Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges