Using a smartphone unavoidably generates masses of digital data that are accessible to others, and these data provide clues to the user’s personality. In a new German study, a team of Ludwig-Maximilians-Universitaet (LMU) researchers led by psychologist Dr. Markus Bühner explored how revealing these clues truly are.

The researchers set out to determine whether conventional data passively collected by smartphones (such as times or frequencies of use) provide insights into users’ personalities. The answer was pretty clear.

“Yes, automated analysis of these data does allow us to draw conclusions about the personalities of users, at least for most of the major dimensions of personality,” said Dr. Clemens Stachl, who used to work with Markus Bühner (Chair of Psychological Methodologies and Diagnostics at LMU) and is now a researcher at Stanford University in California.

The findings are published in the journal PNAS.

For the study, the LMU team recruited 624 volunteers for their PhoneStudy project. The participants completed an extensive questionnaire describing their personality traits, and installed an app that had been developed specially for the study on their phones for 30 days.

The app was developed to collect coded information relating to their behavior. The team was primarily interested in data relating to communication patterns, social behavior and mobility, together with users’ choice and consumption of music, the selection of apps used, and the temporal distribution of their phone usage over the course of the day.

All the data on personality and smartphone use were then analyzed with the help of machine-learning algorithms, which were trained to recognize and extract patterns from the behavioral data, and relate these patterns to the information gleaned from the personality surveys. The ability of the algorithms to predict the personality traits of the users was then cross-validated on the basis of a new dataset.

“By far the most difficult part of the project was the pre-processing of the huge amount of data collected and the training of the predictive algorithms,” said Stachl. “In fact, in order to perform the necessary calculations, we had to resort to the cluster of high-performance computers at the Leibniz Supercomputing Centre in Garching (LRZ).”

The team focused on the five most significant personality dimensions (the Big Five) identified by psychologists, which allowed them to characterize personality differences between individuals in a comprehensive way.

These dimensions include the following: (1) openness (willingness to adopt new ideas, experiences and values), (2) conscientiousness (dependability, punctuality, ambitiousness and discipline), (3) extraversion (sociability, assertiveness, adventurousness, dynamism and friendliness), (4) agreeableness (willingness to trust others, good natured, outgoing, obliging, helpful) and (5) emotional stability (self-confidence, equanimity, positivity, self-control).

The analysis reveals that the algorithm was indeed able to successfully obtain most of these personality traits from smartphone use. Further, the findings offer hints as to which types of digital behavior are most informative for specific self-assessments of personality.

For instance, data relating to communication patterns and social behavior (as reflected by smartphone use) were strongly linked to levels of self-reported extraversion, while information relating to patterns of day and night-time activity was significantly predictive of self-reported degrees of conscientiousness. Notably, links with the category “openness” only became apparent when highly contrasting types of data (e.g., app usage) were combined.

The findings are of great value to researchers, as most studies have been almost exclusively based on self-reports. The conventional method has proven to be sufficiently reliable in predicting levels of professional success, for instance.

“Nevertheless, we still know very little about how people actually behave in their everyday lives — apart from what they choose to tell us on our questionnaires,” said Bühner. “Thanks to their broad distribution, their intensive use and their very high level of performance, smartphones are an ideal tool with which to probe the relationships between self-reported and real patterns of behavior.”

Stachl is aware that his research might further stimulate the appetites of the dominant IT firms for data. In addition to regulating the use of passively collected data and strengthening rights to privacy, we also need to take a comprehensive look at the field of artificial intelligence, he said.

“The user, not the machine, must be the primary focus of research in this area. It would be a serious mistake to adopt machine-based methods of learning without serious consideration of their wider implications. The potential of these applications — in both research and business — is tremendous.

“The opportunities opened up by today’s data-driven society will undoubtedly improve the lives of large numbers of people,” Stachl said. “But we must ensure that all sections of the population share the benefits offered by digital technologies.”

Source: Ludwig-Maximilians-Universitaet