scholarly journals Predicting personality from patterns of behavior collected with smartphones

2020 ◽  
Vol 117 (30) ◽  
pp. 17680-17687 ◽  
Author(s):  
Clemens Stachl ◽  
Quay Au ◽  
Ramona Schoedel ◽  
Samuel D. Gosling ◽  
Gabriella M. Harari ◽  
...  
Keyword(s):  
Big Five ◽  
Behavioral Data ◽  
Big Five Personality ◽  
Night Time ◽  
Time Activity ◽  
Individual Privacy ◽  
Machine Learning Approach ◽  
Social Media Platforms ◽  
Patterns Of Behavior ◽  
Broad Domain

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 (rmedian= 0.37) and narrow facet levels (rmedian= 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.

scholarly journals Age-Related Differences in Actual-Ideal Personality Trait Level Discrepancies

2020 ◽  
Author(s):  
Marie Hennecke ◽  
Paul Schumann ◽  
jule specht
Keyword(s):  
Older Adults ◽  
Big Five ◽  
Personality Trait ◽  
Age Effects ◽  
Big Five Personality ◽  
Age Related ◽  
Trait Level ◽  
Patterns Of Behavior ◽  
Nationally Representative ◽  
The Impact

People differ from each other in their typical patterns of behavior, thought, and emotion and these patterns are considered to constitute their personalities (Funder, 2001). For various reasons, for example because certain trait levels may help to attain certain goals or fulfill certain social roles, people may experience that their actual trait levels are different from their ideal trait levels. In this study, we investigated (1) the impact of age on discrepancies between actual and ideal Big Five personality trait levels and (2) the impact of these discrepancies on personality trait changes across a period of two years. We use data of a large, nationally representative, and age-diverse sample (N = 4,057, 17-94 years, M = 53 years). Results largely confirmed previously reported age effects on actual personality trait levels but were sometimes more complex. Ideal trait levels exceeded actual trait levels more strongly for younger compared to older adults. Unexpectedly, neither ideal trait levels nor their interaction with beliefs about the extent to which personality is malleable vs. fixed predicted trait change over two years (controlling for actual trait levels). We conclude that ideal-actual trait level discrepancies may provide an impetus for change but that they appear to neither alone nor in combination with the belief that personality trait change is possible suffice to produce such change. We discuss commitment, self-efficacy, and strategy knowledge as potential additional predictors of trait change.

The dashang feature in social media: a personality and justice theory perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinlin Wan ◽  
Yaobin Lu ◽  
Sumeet Gupta
Keyword(s):  
Social Media ◽  
Big Five ◽  
Big Five Personality ◽  
Justice Theory ◽  
User Characteristics ◽  
Interpersonal Justice ◽  
Content Type ◽  
Informational Justice ◽  
Social Media Platforms

PurposeDashang refers to a reward given voluntarily to street performers in return for their performance. Some social media platforms have created a way to integrate this as a function, referred to as the dashang feature, to allow users to reward live performers online as well. Over the last few years, this function has become extremely popular among social media users, as it recreates the nostalgic experience of watching street performances. Platforms now consider it indispensable, as it has become a source of substantial revenue (commission on rewards earned by performers). However, not all users reward performers. For each user who pays, there are many more who lurk on the platform. This study examines the reasons for these differences using the Big Five personality perspective and justice theory.Design/methodology/approachWe develop an empirical model using the Big Five theory and justice theory and test it using empirical data collected through a survey of WeChat users.FindingsThe results indicate that distributive justice, interpersonal justice and informational justice are essential factors in relation to social media users' use of the dashang feature. It is also found that personality type affects these three factors.Originality/valueThis study makes three key contributions. First, it examines the factors that influence users' voluntary use of the dashang feature using the lenses of the Big Five theory and justice theory. Second, this study extends previous results on perceived justice to examine use of the dashang feature in social media. Third, this study applies these theories to the study of consumer behavior by exploring the role of user characteristics in social media use.

scholarly journals Relationship Between Personality and Music Preferences Among University Students

2021 ◽  
Vol 13 (2-2) ◽  
Author(s):  
Tai Minnie ◽  
Norashikin Mahmud ◽  
Wan Mohd Azam Wan Mohd Yunus ◽  
Nor Akmar Nordin
Keyword(s):  
University Students ◽  
Personality Traits ◽  
Big Five ◽  
Big Five Personality ◽  
Music Preference ◽  
Music Preferences ◽  
College And University ◽  
Social Media Platforms ◽  
Short Test ◽  
The Relationship

This study analysed the relationship between Big Five personality traits and music preferences among university students. Big Five Inventory (BFI) and Short Test of Music Preferences (STOMP)  was used to assess personality traits and music preferences. Questionnaires were distributed through social media platforms to college and university students aged 19 to 26. A total number of 145 respondents participated in this study. The results showed Agreeableness, Conscientiousness, and Openness were the most prevalent personality traits among respondents and Energetic-Rhythmic (ER) was the most preferred music. The correlation analysis showed that there is a significant correlation between Openness and energetic-rhythmic (ER) music. On the other hand, there was no significant correlation between other traits (Conscientiousness, Extraversion, Agreeableness, and Neuroticism) with music preference dimensions such as Intense-Rebellious (IR) and Energetic-Rhythmic (ER). The inconsistencies in the literature and our findings suggested more studies are needed to understand the influence of personality on music preferences.

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.

Further Validity of the Japanese Version of the Ten Item Personality Inventory (TIPI-J)

2014 ◽  
Vol 35 (4) ◽  
pp. 236-244 ◽  
Author(s):  
Atsushi Oshio ◽  
Shingo Abe ◽  
Pino Cutrone ◽  
Samuel D. Gosling
Keyword(s):  
Big Five ◽  
Content Validity ◽  
Structural Equation ◽  
English Language ◽  
Full Range ◽  
Reliability And Validity ◽  
Japanese Version ◽  
Personality Inventory ◽  
Equation Modeling ◽  
Big Five Personality

The Ten Item Personality Inventory (TIPI; Gosling, Rentfrow, & Swann, 2003 ) is a widely used very brief measure of the Big Five personality dimensions. Oshio, Abe, and Cutrone (2012) have developed a Japanese version of the TIPI (TIPI-J), which demonstrated acceptable levels of reliability and validity. Until now, all studies examining the validity of the TIPI-J have been conducted in the Japanese language; this reliance on a single language raises concerns about the instrument’s content validity because the instrument could demonstrate reliability (e.g., retest) and some forms of validity (e.g., convergent) but still not capture the full range of the dimensions as originally conceptualized in English. Therefore, to test the content validity of the Japanese TIPI with respect to the original Big Five formulation, we examine the convergence between scores on the TIPI-J and scores on the English-language Big Five Inventory (i.e., the BFI-E), an instrument specifically designed to optimize Big Five content coverage. Two-hundred and twenty-eight Japanese undergraduate students, who were all learning English, completed the two instruments. The results of correlation analyses and structural equation modeling demonstrate the theorized congruence between the TIPI-J and the BFI-E, supporting the content validity of the TIPI-J.

Intelligence and Personality

2020 ◽  
Vol 41 (3) ◽  
pp. 124-132
Author(s):  
Marc-André Bédard ◽  
Yann Le Corff
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Real Life ◽  
Big Five Personality Traits ◽  
Original Study ◽  
Life Assessment ◽  
Big Five Personality ◽  
Verbal Intelligence ◽  
Nonverbal Intelligence ◽  
French Canadian

Abstract. This replication and extension of DeYoung, Quilty, Peterson, and Gray’s (2014) study aimed to assess the unique variance of each of the 10 aspects of the Big Five personality traits ( DeYoung, Quilty, & Peterson, 2007 ) associated with intelligence and its dimensions. Personality aspects and intelligence were assessed in a sample of French-Canadian adults from real-life assessment settings ( n = 213). Results showed that the Intellect aspect was independently associated with g, verbal, and nonverbal intelligence while its counterpart Openness was independently related to verbal intelligence only, thus replicating the results of the original study. Independent associations were also found between Withdrawal, Industriousness and Assertiveness aspects and verbal intelligence, as well as between Withdrawal and Politeness aspects and nonverbal intelligence. Possible explanations for these associations are discussed.

Using Principal Component Scores to Enhance the Validity and Reliability of Big Five Personality Measures

2017 ◽  
Vol 38 (2) ◽  
pp. 83-93
Author(s):  
Jeffrey M. Cucina ◽  
Nicholas L. Vasilopoulos ◽  
Arwen H. DeCostanza
Keyword(s):  
Big Five ◽  
Discriminant Validity ◽  
Reliability And Validity ◽  
Principal Component ◽  
Big Five Personality ◽  
Validity And Reliability ◽  
Scoring Method ◽  
Five Factors ◽  
Big Five Factors ◽  
Component Scores

Abstract. Varimax rotated principal component scores (VRPCS) have previously been offered as a possible solution to the non-orthogonality of scores for the Big Five factors. However, few researchers have examined the reliability and validity of VRPCS. To address this gap, we use a lab study and a field study to investigate whether using VRPCS increase orthogonality, reliability, and criterion-related validity. Compared to the traditional unit-weighting scoring method, the use of VRPCS enhanced the reliability and discriminant validity of the Big Five factors, although there was little improvement in criterion-related validity. Results are discussed in terms of the benefit of using VRPCS instead of traditional unit-weighted sum scores.

How Do People Share Their Positive Events?

2016 ◽  
Vol 37 (4) ◽  
pp. 250-259 ◽  
Author(s):  
Cara A. Palmer ◽  
Meagan A. Ramsey ◽  
Jennifer N. Morey ◽  
Amy L. Gentzler
Keyword(s):  
Cluster Analysis ◽  
Communication Technology ◽  
Big Five ◽  
Well Being ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Positive Events ◽  
Growing Body ◽  
Sharing Patterns ◽  
Practical Implications

Abstract. Research suggests that sharing positive events with others is beneficial for well-being, yet little is known about how positive events are shared with others and who is most likely to share their positive events. The current study expanded on previous research by investigating how positive events are shared and individual differences in how people share these events. Participants (N = 251) reported on their likelihood to share positive events in three ways: capitalizing (sharing with close others), bragging (sharing with someone who may become jealous or upset), and mass-sharing (sharing with many people at once using communication technology) across a range of positive scenarios. Using cluster analysis, five meaningful profiles of sharing patterns emerged. These profiles were associated with gender, Big Five personality traits, narcissism, and empathy. Individuals who tended to brag when they shared their positive events were more likely to be men, reported less agreeableness, less conscientiousness, and less empathy, whereas those who tended to brag and mass-share reported the highest levels of narcissism. These results have important theoretical and practical implications for the growing body of research on sharing positive events.

Personality of Clown Doctors

2016 ◽  
Vol 37 (1) ◽  
pp. 49-55 ◽  
Author(s):  
Alberto Dionigi
Keyword(s):  
General Population ◽  
Personality Traits ◽  
Big Five ◽  
Normal Population ◽  
Big Five Personality Traits ◽  
Published Data ◽  
Big Five Personality

Abstract. In recent years, both professional and volunteer clowns have become familiar in health settings. The clown represents a peculiar humorist’s character, strictly associated with the performer’s own personality. In this study, the Big Five personality traits (BFI) of 155 Italian clown doctors (130 volunteers and 25 professionals) were compared to published data for the normal population. This study highlighted specific differences between clown doctors and the general population: Clown doctors showed higher agreeableness, conscientiousness, openness, and extraversion, as well as lower neuroticism compared to other people. Moreover, specific differences emerged comparing volunteers and professionals: Professional clowns showed significantly lower in agreeableness compared to their unpaid colleagues. The results are also discussed with reference to previous studies conducted on groups of humorists. Clowns’ personalities showed some peculiarities that can help to explain the facility for their performances in the health setting and that are different than those of other groups of humorists.

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