“Just the Way You Are”: Linking Music Listening on Spotify and Personality

2020 ◽  
pp. 194855062092322
Author(s):  
Ian Anderson ◽  
Santiago Gil ◽  
Clay Gibson ◽  
Scott Wolf ◽  
Will Shapiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Traits ◽  
Meta Analysis ◽  
Streaming Data ◽  
Self Report ◽  
Big Five Personality ◽  
Music Listening ◽  
Listening Behavior ◽  
Listening Behaviors ◽  
Musical Preferences

Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.

scholarly journals Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits

2019 ◽  
Author(s):  
Clemens Stachl ◽  
Quay Au ◽  
Ramona Schoedel ◽  
Daniel Buschek ◽  
Sarah Völkel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Personality Traits ◽  
Big Five ◽  
Big Five Personality Traits ◽  
Self Report ◽  
Behavioral Data ◽  
Big Five Personality ◽  
Behavioral Patterns ◽  
Smartphone Sensing

The understanding, quantification and evaluation of individual differences in behavior, feelings and thoughts have always been central topics in psychological science. An enormous amount of previous work on individual differences in behavior is exclusively based on data from self-report questionnaires. To date, little is known about how individuals actually differ in their objectively quantifiable behaviors and how differences in these behaviors relate to big five personality traits. Technological advances in mobile computer and sensing technology have now created the possiblity to automatically record large amounts of data about humans' natural behavior. The collection and analysis of these records makes it possible to analyze and quantify behavioral differences at unprecedented scale and efficiency. In this study, we analyzed behavioral data obtained from 743 participants in 30 consecutive days of smartphone sensing (25,347,089 logging-events). We computed variables (15,692) about individual behavior from five semantic categories (communication & social behavior, music listening behavior, app usage behavior, mobility, and general day- & nighttime activity). Using a machine learning approach (random forest, elastic net), we show how these variables can be used to predict self-assessments of the big five personality traits at the factor and facet level. Our results reveal distinct behavioral patterns that proved to be differentially-predictive of big five personality traits. Overall, this paper shows how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality.

The Relationship Between Narcissism and Personality Traits of the Five-Factor-Model in Adolescents and Young Adults

2018 ◽  
Vol 46 (6) ◽  
pp. 516-522 ◽  
Author(s):  
Marc Allroggen ◽  
Peter Rehmann ◽  
Eva Schürch ◽  
Carolyn C. Morf ◽  
Michael Kölch
Keyword(s):  
Young Adults ◽  
Personality Traits ◽  
Five Factor Model ◽  
Self Report ◽  
Adolescents And Young Adults ◽  
Personality Factors ◽  
Big Five Personality ◽  
Grandiose Narcissism ◽  
Vulnerable Narcissism ◽  
The Relationship

Abstract.Narcissism is seen as a multidimensional construct that consists of two manifestations: grandiose and vulnerable narcissism. In order to define these two manifestations, their relationship to personality factors has increasingly become of interest. However, so far no studies have considered the relationship between different phenotypes of narcissism and personality factors in adolescents. Method: In a cross-sectional study, we examine a group of adolescents (n = 98; average age 16.77 years; 23.5 % female) with regard to the relationship between Big Five personality factors and pathological narcissism using self-report instruments. This group is compared to a group of young adults (n = 38; average age 19.69 years; 25.6 % female). Results: Grandiose narcissism is primarily related to low Agreeableness and Extraversion, vulnerable narcissism to Neuroticism. We do not find differences between adolescents and young adults concerning the relationship between grandiose and vulnerable narcissism and personality traits. Discussion: Vulnerable and grandiose narcissism can be well differentiated in adolescents, and the pattern does not show substantial differences compared to young adults.

scholarly journals A meta-analysis on individual differences in primary emotional systems and Big Five personality traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Davide Marengo ◽  
Kenneth L. Davis ◽  
Gökçe Özkarar Gradwohl ◽  
Christian Montag
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Big Five ◽  
Mammalian Species ◽  
Theoretical Perspective ◽  
Big Five Personality Traits ◽  
Self Report ◽  
Big Five Personality ◽  
Affective Neuroscience ◽  
Emotional Systems

AbstractThe Affective Neuroscience Personality Scales (ANPS) were constructed as a self-report assessment to measure individual differences in Jaak Panksepp’s cross-species primary emotional systems: SEEKING, PLAY, CARE (positive emotions) and FEAR, SADNESS, ANGER (negative emotions). Beginning with the first published work on the ANPS in 2003, individual differences on the ANPS measures of these six primary emotional systems have been consistently linked to Big Five personality traits. From a theoretical perspective, these primary emotional systems arising from subcortical regions, shed light on the nature of the Big Five personality traits from an evolutionary perspective, because each of these primary emotional systems represent a tool for survival endowing mammalian species with inherited behavioral programs to react appropriately to complex environments. The present work revisited 21 available samples where both ANPS and Big Five measures have been administered. Our meta-analytical analysis provides solid evidence that high SEEKING relates to high Openness to Experience, high PLAY to high Extraversion, high CARE/low ANGER to high Agreeableness and high FEAR/SADNESS/ANGER to high Neuroticism. This seems to be true regardless of the ANPS inventory chosen, although much more work is needed in this area. Associations between primary emotional systems and Conscientiousness were in the lower effect size area across all six primary emotions, thereby supporting the idea that Conscientiousness rather seems to be less directly related with the subcortical primary emotions and likely is the most cognitive/cortical personality construct out of the Big Five. In sum, the present work underlines the idea that individual differences in primary emotional systems represent evolutionarily ancient foundations of human personality, given their a) meaningful links to the prominent Big Five model and b) their origins lying in subcortical areas of the human brain.

scholarly journals Preventive and Curative Personality Profiling Based on EEG, ERP, and Big Five Personality Traits: A Literature Review

2020 ◽  
Vol 17 (2) ◽  
pp. 531-545
Author(s):  
Cut Amalia Saffiera ◽  
Raini Hassan ◽  
Amelia Ritahani Ismail
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Design ◽  
Personality Traits ◽  
Big Five ◽  
Healthy Lifestyle ◽  
Big Five Personality Traits ◽  
Event Related Potential ◽  
Big Five Personality ◽  
Electrophysiological Signals

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning.

scholarly journals To Fear or Fly to the Unknown: Tolerance for Ambiguity and Big Five Personality Traits

2019 ◽  
Author(s):  
Hayley Jach ◽  
Luke Smillie
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Openness To Experience ◽  
Self Report ◽  
Big Five Personality ◽  
Aesthetic Appreciation ◽  
Ambiguity Tolerance ◽  
Intellectual Curiosity ◽  
Behavioral Choice ◽  
Tolerance For Ambiguity

The present study investigated whether ambiguity tolerance relates to personality traits that are theoretically grounded in fear (neuroticism) or attraction (openness to experience; extraversion) for the unknown. Our hypotheses were supported for self-report measures (and openness to experience predicted ambiguity tolerance controlling for intelligence), but behavioral choice measures of ambiguity tolerance demonstrated poor reliability and were unrelated to self-reported ambiguity tolerance and basic personality traits. An exploratory network analysis revealed that ambiguity tolerance was more strongly related to the intellectual curiosity (vs. aesthetic appreciation) facet of openness to experience, and the assertiveness (vs. energy or sociability) facet of extraversion. Our findings reinforce the fragmented literature in this area, and support predictions derived from psychological entropy theories of personality.

scholarly journals Loneliness and the Big Five Personality Traits: A Meta–Analysis

2020 ◽  
Vol 34 (1) ◽  
pp. 8-28 ◽  
Author(s):  
Susanne Buecker ◽  
Marlies Maes ◽  
Jaap J. A. Denissen ◽  
Maike Luhmann
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Structural Equation ◽  
Variance Estimation ◽  
Meta Analysis ◽  
The Other ◽  
Personality Factors ◽  
Big Five Personality ◽  
Personality Psychology ◽  
Total N

This preregistered meta–analysis ( k = 113, total n = 93 668) addressed how the Big Five dimensions of personality (extraversion, agreeableness, conscientiousness, neuroticism, and openness) are related to loneliness. Robust variance estimation accounting for the dependency of effect sizes was used to compute meta–analytic bivariate correlations between loneliness and personality. Extraversion ( r = −.370), agreeableness ( r = −.243), conscientiousness ( r = −.202), and openness ( r = −.107) were negatively related to loneliness. Neuroticism ( r = .358) was positively related to loneliness. These associations differed meaningfully in strength depending on how loneliness was assessed. Additionally, meta–analytic structural equation modelling was used to investigate the unique association between each personality trait and loneliness while controlling for the other four personality traits. All personality traits except openness remained statistically significantly associated with loneliness when controlling for the other personality traits. Our results show the importance of stable personality factors in explaining individual differences in loneliness. © 2020 European Association of Personality Psychology

scholarly journals DO PERSONALITY TRAITS INFLUENCE PERCEPTIONS OF COGNITIVE CHANGE IN COMMUNITY DWELLING OLDER ADULTS?

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S893-S894
Author(s):  
Thomas M Meuser ◽  
Regula H Robnett
Keyword(s):  
Personality Traits ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Cognitive Change ◽  
Community Dwelling ◽  
Self Report ◽  
Sample Population ◽  
Big Five Personality ◽  
Cognitive Screening ◽  
Influence Perceptions

Abstract Recent research has linked personality traits and risk for cognitive impairment in advancing age. Associations with neuroticism are particularly robust. Both longstanding and recent elevations may predict dementia. Other traits – conscientiousness and openness to experience – also show unique associations. These findings derive mainly from large sample population studies and smaller clinical investigations. Relevance to the general population is unclear. We investigated the “big five” personality traits and cognition in 232 community-dwelling adults (73% female, 97% Caucasian, mean age 72 years). Scores on a self-report screen for dementia – the AD8 – framed the sample: 77% scored 0 points, no dementia; 23% scored 2+, possible dementia. Age and personality were independent variables in a binary logistic regression with AD8 status as dependent. All predictors but one, extraversion, were significant (p < .05), suggesting that personality traits may influence perceptions of cognitive change. Higher agreeableness and neuroticism predicted possible dementia status on the AD8, whereas higher openness and conscientiousness predicted normal cognition. Interestingly, most in the AD8 positive group (70%) denied having “more problems with memory than most” on the Geriatric Depression Scale. These perceptions would seem incompatible, especially for true positive cases. Our findings suggest that the role of personality in dementia screening (and, perhaps, diagnosis) may be more nuanced than indicated in other studies. Longstanding traits and present perceptions are both elements of the evaluative process, as much as test scores and reported history. Our findings speak to the value of a person-centered, context-aware approach in cognitive screening.

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