Predicting demographics, moral foundations, and human values from digital behaviours

Abstract

Personal electronic devices including smartphones give access to behavioural signals that can be used to learn about the characteristics and preferences of individuals. In this study, we explore the connection between demographic and psychological attributes and the digital behavioural records, for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income.Along with the demographic data, we collected self-reported assessments on validated psychometric questionnaires for moral traits and basic human values, and combined this information with passively collected multi-modal digital data from web browsing behaviour and smartphone usage. A machine learning framework was then designed to infer both the demographic and psychological attributes from the behavioural data. In a cross-validated setting, our models predicted demographic attributes with good accuracy as measured by the weighted AUROC score (Area Under the Receiver Operating Characteristic), but were less performant for the moral traits and human values.These results call for further investigation, since they are still far from unveiling individuals' psychological fabric.This connection, along with the most predictive features that we provide for each attribute, might prove useful for designing personalised services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldview.

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

“…There is an ever-increasing interest in moral values understanding since they reflect our perception, attitudes, and opinion formation on critical societal issues. Moral values are expressed in user-generated content [49], and primarily through text. With the burst of social media data, emerges a unique opportunity of observing such behaviors in scale and as they happen.…”

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“…При помощи специальных алгоритмов удается с большой точностью выявлять черты личности, мотивы, ценности и др. (Azucar et al, 2018;Guntuku et al, 2017;Kalimeri et al, 2019). Пока эта область исследования не получила общепринятого наименования, для ее определения используются различные термины: цифровая психометрика (digital psychometrics) (Matz et al, 2017b), психологическая информатика (psycho-informatics) (Markowetz et al, 2014).…”

“…(Azucar et al, 2018;Park et al, 2015). Существенно реже прогнозируются другие психологические характеристики: ценности, мотивы, интеллект, психологическое благополучие человека (Kalimeri et al, 2019;Kosinski et al, 2013).…”

“…Подобные результаты, по-видимому, обусловлены особенностями самого понятия «ценности»: по сравнению с чертами личности и состояниями оно более обобщенное и абстрактное. Кроме того, влияние ценностей на поступки и суждения людей проявляется в более узком спектре ситуаций (например, при принятии достаточно важных решений - Kalimeri et al, 2019).…”

Traditional versus Facebook-based surveys: Evaluation of biases in self-reported demographic and psychometric information

“…Social sciences are going through a revolution, in light of the immense possibilities that arise from the ability to observe real-world human behaviours via digital data, timely, and at a grander scale. Digital data offer rich, fine-grained individual information at a population level depicting a complementary view of the society (Lazer et al 2009;Kalimeri et al 2019a), especially when official records are sparse or unavailable. A rapidly growing body of research now employs social media data as a proxy to address challenging demographic research questions of a wide range of social issues.…”