Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

An analysis of association of personality traits with job burnout among nurses working in hospital

Objective:The objective of the present study is to explore the effects of personality traits on job burnout among hospital nurses.Materials and Methods:This cross-sectional research was done during 2019-2020 at Kashan Shahid Beheshti Hospital.The data analysis procedures included descriptive statistics and the partial least squares-based structural equation modeling.The participants were 150 nursing professionals.A questionnaire indicating information on demographics,burnout(measured using the Maslach Burnout Inventory with three dimensions of depersonalization,emotional exhaustion,and personal accomplishment),and personality profile(measured employing the neuroticism extraversion openness five-factor inventory including extroversion,conscientiousness,agreeableness,neuroticism,and openness to experience dimensions)was used to gather the required data.Results:The results of the study showed that the validity and reliability of the measurement model were desirable(factor load higher than 0.5,the Cronbach’s alpha value and the composite reliability are>0.7).Structural model showed statistically drastic,negative relationship between the nurses’burnout levels and neuroticism(β=0.722)and openness to experience(β=0.437).However,the relationship was significantly positive between the nurses’burnout levels and conscientiousness(β=0.672),agreement(β=0.594),and extraversion(β=0.559)(P<0.03).Conclusions:The present study helped the recognition of burnout among nurses working in hospitals and approved the effects of personality features on the burnout experience.

Personality Traits, Thinking Styles, and Emotional Intelligence in Nursing, towards Healthcare Providers’ Characterization and Safer Patient Care

Background: This study explored nursing personality traits (Big Five Inventory BFI), emotional intelligence (EI), and thinking styles (Rational, RS, and Experiential, ES) together with demographic data to see how they could relate and the implication of this on nurses and patient safety. Design: A cross-sectional study. Methods: Nursing sample (n = 435). Participants completed a self-report online survey, which included demographic information, followed by questionnaires to measure personality traits, thinking styles, and emotional intelligence. Results: Spearman’s rank correlation was computed to assess the relationship between EI and Extraversion;there was a moderate positive correlation between the two variables, r = 0.487, p r = 0.731, p r = 0.723, p r = -0.666, p r = 0.467, p Conclusion: Different studies consolidated each other, and all converge and channel into the concept of characterization of healthcare providers for better support to them and safer patient care. EI correlated with all BFI components, and both positively impacted all desirable behaviors. Therefore, it would be valuable if organizations invested in increasing EI in their providers as it might highlight areas for improvement and equip providers with appropriate and advantageous coping strategies.

Understanding Nonverbal Communication Cues of Human Personality Traits in Human-Robot Interaction

With the increasing presence of robots in our daily life,there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand users’mood,intention,and other aspects.During human-human interaction,personality traits have an important influence on human behavior,decision,mood,and many others.Therefore,we propose an efficient computational framework to endow the robot with the capability of understanding the user’s personality traits based on the user’s nonverbal communication cues represented by three visual features including the head motion,gaze,and body motion energy,and three vocal features including voice pitch,voice energy,and mel-frequency cepstral coefficient(MFCC).We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions,and meanwhile,the robot extracts the nonverbal features from each participant’s habitual behavior using its on-board sensors.On the other hand,each participant’s personality traits are evaluated with a questionnaire.We then train the ridge regression and linear support vector machine(SVM)classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers.We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues.

Personality traits and self-harm behaviors among Chinese children and adolescents: The mediating effect of psychological resilience

BACKGROUND Previous studies have shown that personality traits are associated with self-harm(SH)in adolescents.However,the role of resilience in this association remains unclear.Our research aims to explore the hypothesized mediation effect of resilience in the relationship between personality traits and SH in Chinese children and adolescents.AIM To evaluate resilience as a mediator of the association between personality traits and SH.METHODS A population-based cross-sectional survey involving 4471 children and adolescents in Yunnan province in southwestern China was carried out.Relevant data were collected by self-reporting questionnaires.Univariate and multivariate logistic regression models were employed to identify associated factors of SH.A path model was used to assess the mediation effect of resilience with respect to personality traits and SH association.RESULTS Among the 4471 subjects,1795 reported SH,with a prevalence of 40.1%(95%CI:34.4%-46.0%).All dimensions of personality traits were significantly associated with SH prevalence.Resilience significantly mediated the associations between three dimensions of personality(extroversion,neuroticism,psychoticism)and SH,accounting for 21.5%,4.53%,and 9.65%,respectively,of the total associations.Among all dimensions of resilience,only emotional regulation played a significant mediation role.CONCLUSION The results of the study suggest that improving emotion regulation ability might be effective in preventing personality-associated SH among Chinese children and adolescents.

An Analysis of Donald Trump's Political Personality Traits and Policy Orientations

As a political leader, US President Trump's personality traits affect his policy orientations and current US foreign policy. The authors analyze Trump's personality in several categories—uninhibited and capricious, dynamic and capable, profit-orientated and self-centered,competitive and persistent, positive and extraverted. The traits of breaking traditions, skill at strategic deception and negotiation, action-motivated implementation, intuitive decision-making, pursuit of respect and interest exchange, and vengefulness will shape his policy and behavioral orientations. Initial study shows Trump to be a political leader with positive personality traits and double-sided dimensions. The analysis offers insight toward understanding the new US executive and his policy direction.

No More Excuses - Personality Traits and Academic Dishonesty in Online Courses

Academic dishonesty is a disturbing issue in higher education that has been worsening over the years, especially with the appearance of the internet and the e-learning education. This new technology exposes students to the opportunity of using online bank exams and term papers and increases their tendency to cheat. This study investigates student academic dishonesty in the context of traditional and distance-learning courses in higher education. Data from 1,365 students enrolled in academic institutes in the U.S.A and Israel were surveyed to assess their personality and their willingness to commit various acts of academic misconduct. The findings indicate that in both countries dishonest behaviors are greater in face-to-face courses than in online courses. In addition, both American and Israeli students identified with the personality trait of Agreeableness showed a negative correlation with academic dishonesty. Furthermore, Israeli students identified with the personality traits of Conscientiousness and Emotional Stability demonstrated a negative correlation with academic dishonesty. In contrast, the personality trait of Extraversion among American students was found in a positive correlation with academic misconduct. Implications for further research are discussed.

The Impact of Personality Traits and Problem Structures on Management Decision-Making Outcomes

Since decision-making behavior has been in the focus both from a scientific and a professional position, there seems to be a dispute whether rational or intuitive decision making leads to better outcomes. By now, scholars have agreed that effective organizations do not have the luxury to choose between the ＂applications＂ of intuitive or rational decision making. Instead, they try to understand how different factors like personality traits and problem characteristics influence the decision-making process. Reviewing the literature reveals that personality pre-determination and the structure of problems （e.g., well-structured problems （WSPs） versus ill-structured problems （ISPs）） seem to have a significant impact on the decision-making efficiency. Further, the review also shows that there is a lack of application-oriented empirical studies in this area of research. Therefore, the aim of this research paper is to propose a framework for an empirical study on how personality traits and problem structure influence the decision-making process. First, hypotheses are derived from the literature on how personality pre-determination and behavioral patterns in the decision-making process lead to higher socioeconomic efficiency within certain problem categories. Second, a causal model and a setup for a laboratory experiment are proposed to allow testing the hypotheses. Finally, the conclusions provide an outlook on how this research could support organizations in their decision-making processes.

Relationship of Smartphone Dependence to General Health Status and Personality Traits among University Students

We investigated associations between smartphone dependence and general health status or personality traits. To 197 medical university students, we administered a set of self-reporting questionnaires designed to evaluate these parameters. For males, smartphone dependence positively correlated with somatic symptoms, anxiety and insomnia, and emotional instability, and negatively correlated with agreeableness. For females, smartphone dependence positively correlated with somatic symptoms, severe depression, and extraversion, and negatively correlated with social dysfunction. These findings suggest that smartphone dependence may be associated with general health status or personality traits and that there may be a gender difference in these associations.

The Correlation between Synthetic Oxytocin Given during Labor and Women’s Personality Traits According to the SSP (Swedish University Scales of Personality) Survey: A Pilot Study

Background: In modern obstetric care, oxytocin is one of the most frequently used drugs, and the possible mental impact this drug has on women is very little studied. The objective of this study is to investigate whether women augmented with oxytocin during labor will rate their personality profile differently after childbirth than non-stimulated women. Methods: Prospective cohort study was performed at Women’s Clinic, Soder hospital, Stockholm.76 women received the SSP (Swedish University Scales of Personality) questionnaire to fill in during their stay in the post-maternity ward after labor. Information about the use of oxytocin was retrieved from the women’s medical records. Primary outcome: Differences in the SSP scores in the group augmented with synthetic oxytocin during labor compared with the non-augmented group. Results: Women with and without oxytocin estimates on the SSP subscale form differed regarding personality traits described as “lack of assertiveness” (p = 0.04), which means “lack of ability to speak up and to be self-assertive in social situations”. The result also showed that women that had a long time of augmentation with oxytocin (>5 h) scored higher for “social desirability” (p = 0.004), which was defined as being “socially adapted,” “friendly,” and “helpful”. A difference in “psychological anxiety” (p = 0.04) and “social desirability” (p = 0.004) was found among women who had oxytocin in a dose of at least 200 ImU/h for ≥1 hour. This group also had a lower rate of “mental anxiety” than those who received lower oxytocin doses. Conclusion: Synthetic oxytocin given during labor may affect the woman mentally. The total time and volume of given oxytocin seem to be essential factors when discussing augmentation’s maternal psychological response. We conclude that prolonged and extended use of synthetic oxytocin during labor should be avoided if possible.

Probing the Relationship Between Language Learners＇ Personality Traits and Their Preferences for Six Category Intervention Analysis： A Focus on Introversion-Extroversion and Sensing-Intuitive Traits

The present paper investigates the relationship between Iranian intermediate level EFL （English as a Foreign Language） learners＇ personality traits and their preferences for Heron＇s Six Category Intervention Analysis （SCIA）. There were 134 Iranian male and female learners participating in this study. A SCIA questionnaire containing 30 items was developed and validated to assess EFL learners＇ preferences for intervention categories. Moreover, Myers-Briggs Type Indicator （MBTI） was used in the research. Results indicated that whereas in some cases no significant differences were observed, there mostly existed numerous significant differences among language learners with different personality traits and their preferences for SCIA.

A Survey for Exploring Personality Traits of Excessive Online Game Users in Japan

For the aim at exploring differences on personality traits between excessive online game users and non-excessive users in Japan, an online survey was conducted using psychological scales measuring addictive tendencies for online gaming, depressive tendencies, aggression, and self-concealment. The results revealed that excessive online game users having addictive tendencies in Japan had lack of self-esteem as one of depressive tendencies, high aggressive tendencies, and low tendency of self-disclosure, in contrast with non-excessive users. Moreover, it was suggested that these users had a confidence in the society based on contributive behaviors in multi-player groups of online gaming.

Assessment of Personality Traits of Physical Education Teachers

The purpose of this study is to examine the relationship between the personality traits of physical education teachers working in secondary schools in Istanbul. The research was conducted in accordance with screening model. The research group is formed by physical education teachers who are working in public and private secondary schools which are connected to the Ministry of National Education in Istanbul in 2012-2013 academic years. Among the participants, 36.8% is formed by female physical education teachers, and 63.2% is formed by male physical education teachers. PERI （personality inventory） short form is used. PERI is formed by five subcomponents： openness to experience, sense of responsibility, extroversion, coherence and emotional stability. Assessment instrument was applied to participants with the face-to-face technique by the researcher. Findings were obtained by using T Test, Kruskal-Wallis Test and Mann-Whitney U Test in the analysis of research data. Consequently, it is understood that physical education teachers had low scores on emotional stability personality traits and so they were not adequate. In openness to experience, extroversion, coherence and sense of responsibility subcomponents, it is understood that they had higher scores and they were adequate. Besides, there is no significant difference found between gender, school type and years of service variables and personality traits （P 〈 0.05）.

Relationship between Sexual Fantasy,Sexual Communication,Personality Traits and Sexual Satisfaction in Married Individuals

The study aimed to understand the relationship between sexual fantasy,sexual communication,personality traits and sexual satisfaction in married individuals.Sexual fantasy as a variable has seldom been studied in the Indian context.The importance of sexual fantasies has been noted by therapists and researchers.Studying various aspects of sexual functioning in married life including,sexual communication and sexual satisfaction and personality traits would be beneficial.A cross sectional design with a total sample of 100 married individuals was considered.Tools were administered as online forms.Parametric and Non-parametric tests were used to find the correlation between Sexual fantasy and sexual satisfaction,sexual communication and sexual satisfaction and personality traits and sexual satisfaction.Results indicated that sexual fantasy and sexual satisfaction have a negative correlation,sexual communication and sexual satisfaction have a positive correlation and personality traits and sexual satisfaction also have a positive correlation.This study can be used to develop modules that might aid in marital and sex therapy.It may be useful in identifying any difficulties or issues which may help in providing appropriate timely intervention.

Who Are You Meant to Be? Predicting Psychological Indicators and Occupations based on Personality Traits

Recognition of psychological characteristics based on massive data and computer machine learning algorithms has gradually become a new way for psychological research. As we all know, person-job fit is an important consideration in recruitment and selection. Most existing selection process can reliably measure skills fit, i.e., matching job seekers’ skills/work experience with job demand. What is often harder to assess is the compatibility between job seekers’ motivational needs/career aspirations and job characteristics, which will ultimately determine their career progress and job satisfaction. With the increasing application of machine learning methods in psychology, this paper constructed classification models to predict individuals’ needs, career aspiration, and occupation through their personality traits. This enables automatic access to individuals’ psychological indicators, with the MLP (Multi-Layer Perceptron) method showing the highest prediction accuracy. In addition, it conducted a comparative analysis of the distribution of personality characteristics in different occupations. Based on the study results, we put forward some countermeasures and suggestions for application in human resource management.

Is tax evasion macho?Testosterone,personality traits and evasion

This paper examines the potential effects of testosterone and personality traits on the decision to evade taxes.In a series of experiments,subjects completed behavioural tasks and made a one-shot tax evasion decision.We estimate a negative weakly significant treatment effect,which suggests that an exogenous increase in the testosterone level may inhibit the decision to evade taxes.Our results also suggest that higher dominance and independent self-construal,as well as lower self-control,are associated with a greater likelihood of tax evasion.We discuss the mechanisms potentially linking testosterone to tax evasion.These findings support the inclusion of biological factors in the analysis of tax evasion behaviour.

Physiognomy： Personality Traits Prediction by Learning

Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.