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84 results for “personality traits”
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Replication Package: Exploring the Relationship Between Personality Traits and User Feedback
<p>This is the replication package for the paper titled 'Automated User Feedback Analysis: Processing the Feedback Quantity and Quality' accepted for the AffectRE23 workshop track at RE 2023.</p> <p>Full abstract:</p> <p>Previous research has studied the impact of developer personality in different software engineering scenarios, such as team dynamics and programming education. However, little is known about how user personality affect software engineering, particularly user-developer collaboration. Along this line, we present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. They also filled out a questionnaire for the Five Factor Model (FFM) personality test. We observed some isolated effects of neuroticism on user feedback: most notably a significant correlation between neuroticism and feedback elaborateness; and between neuroticism and the rating of certain features. The results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users. This and possibly other personality characteristics should be considered when leveraging feedback analytics for software requirements engineering.</p>
Agreeableness personality trait and social information encoding
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Data for Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men
<p>These are the data and metadata for the article </p> <p><strong>Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men</strong></p> <p>by </p> <p><strong>Gerhard Gmel, Simon Marmet, Joseph Studer, and Matthias Wicki</strong></p> <p><strong>to be published in Frontiers of Psychiatry</strong></p>
Personality, sperm traits and a test for their combined dependence on male condition in guppies
<p>There is evidence that animal personality traits can have spill-over effects for sexual selection, with studies reporting that male behavioural types are associated with success during pre- and post-copulatory sexual selection. Given these links between personality and sexual traits, and the evidence that their expression can depend on an individual's nutritional status (i.e. condition), a novel prediction is that changes in a male's diet should alter both the average expression of personality and sexual traits, and their covariance. We tested these predictions using the guppy Poecilia reticulata, a species previously shown to exhibit strong condition dependence in ejaculate traits and a positive correlation between sperm production and individual variation in boldness. Contrary to expectation, we found that dietary restriction – when administered in mature adult males – did not affect the expression of either behavioural (boldness and activity) or ejaculate traits, although we did find that males subjected to dietary stress exhibited a positive association between sperm velocity and boldness that was not apparent in the unrestricted diet group. This latter finding points to possible context-dependent patterns of covariance between sexually selected and personality traits, which may have implications for patterns of selection and evolutionary processes under fluctuating environmental conditions.</p>
Interspecific competition affects the expression of personality-traits in natural populations
<p>Interspecific competition can cause niche partitioning among species in a community and shape an individual’s phenotype, including its behaviour. However, little is known about how interspecific competition affects the expression of animal personality. We investigated whether the occurrence of competing alien grey squirrels (<em>Sciurus carolinensis</em>) altered personality traits in native Eurasian red squirrels (<em>Sciurus vulgaris</em>). We compared personality traits of red squirrels in red-only (no interspecific competition) and red-grey (with interspecific competition) sites, using arena tests. Open Field Test returned repeatable estimates of activity and shyness, Mirror Image Stimulation test of sociability and avoidance of a conspecific. Red squirrels competing with the alien species had higher sociability and slightly lower avoidance than in red-only sites. Moreover, an individual’s probability to survive was not affected by any of the personality traits, but more active females were more likely to wean a litter than shy ones. This relationship was not affected by the presence or absence of the alien competitor. Our findings suggest that the occurrence of certain personality traits in red squirrels was affected by interspecific competition not as a consequence of short-term selection, but more likely as a result of context-related advantages of sociability or avoidance.</p>
Analysis Script of Prediction of Self-efficacy in Recognizing Deepfake based on HEXACO Personality Traits
<p><em><strong>Analysis Script (JASP) </strong></em>of Prediction of Self-efficacy in Recognizing Deepfake based on HEXACO Personality Traits </p>
Personality, sperm traits and a test for their combined dependence on male condition in guppies
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A migratory sparrow has personality in winter that is independent of other traits
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Data from: Personality traits change after an opportunity to mate
<p>There is growing evidence that personality traits can change throughout the life course in humans and nonhuman animals. However, the proximate and ultimate causes of personality trait change are largely unknown, especially in adults. In a controlled, longitudinal experiment, we tested whether a key life event for adults – mating – can cause personality traits to change in female threespine sticklebacks. We confirmed that there are consistent individual differences in activity, sociability and risk taking, and then compared these personality traits among three groups of females: 1) control females; 2) females that physically mated; 3) females that socially experienced courtship but did not mate. Both the physical experience of mating and the social experience of courtship caused females to become less willing to take risks and less social. To understand the proximate mechanisms underlying these changes, we measured levels of excreted steroids. Both the physical experience of mating and the social experience of courtship caused levels of dihydroxyprogesterone (<span>17α,20β-P)</span> to increase, and females with higher <span>17α,20β-P </span>were less willing to take risks and less social. These results provide experimental evidence that personality traits and their underlying neuroendocrine correlates are influenced by formative social and life-history experiences well into adulthood.</p>
Short and long-term effects of endogenous cortisol on personality traits and behavioral syndromes
<p>Animals express consistent individual differences in some behaviours, termed animal personality but behaviours can also considerably vary within individuals, within minutes or hours, due to environmental stimuli. Consistent among-individual variation is often assumed to be mediated by hormonal mechanisms. Hormones are also involved in flexible and fast responses towards environmental stimuli. Even though basic mechanisms by which hormones regulate behaviours are known, much of the quantitative patterns underlying hormone-behaviour interactions within and among individuals, remain unclear. Here, we conducted two experiments to investigate the immediate, short-term effects of experimentally elevated cortisol titres on well-known animal personality traits (Experiment 1) and the potential long-term effects of such experimentally elevated cortisol titres (Experiment 2) in the medium-sized cavy (<em>Cavia aperea</em>). Therefore, we tested how personality traits related to stress-coping, novelty seeking and social behaviour react within hours towards elevated cortisol. In Experiment 2, we tested if a three-weeks elevation of cortisol affects the same personality traits after cessation of the hormone treatment. We investigated effects on the mean levels of behaviours, i.e., the personality type, the temporal consistency, i.e., repeatability and among-individual correlations of traits. In experiment 1, we found cortisol to lead to more aggressive behaviour and more passive stress-coping while other traits were unaffected. In experiment 2, we found no long-term persisting effects. Both measured hormones, cortisol and testosterone, showed correlations to several personality traits, these correlations were, however, unaffected by the cortisol treatment. Animals receiving the cortisol treatment showed higher repeatability, for one stress-coping trait and lower repeatability for testosterone concentration. Interestingly, sexes differed only in few mean trait expressions but showed different correlation structures across traits. Taken together, our data indicate that personality traits in adult individuals are very consistent and only react via short-term fluctuations towards internal hormonal signals.</p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.</p> <p>The folders contain:</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p><br> /notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results</p> <p> Statistics - Polynomial Regression: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p><br> We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
The Relationship between Self-Perception of Smile Aesthetics and Personality Traits of University Students: A Cross-Sectional Study
<p>Los documentos contienen la base de datos que se obtuvo del estudio "The Relationship between Self-Perception of Smile Aesthetics and Personality Traits of University Students: A Cross-Sectional Study", siendo datos reales de estudiantes de una universidad nacional de Perú. Luego se encuentra el documento con los instrumentos, en formato como se presentó para la ejecución. </p> <p> </p>
Dataset of Prediction of Self-efficacy in Recognizing Deepfake based on Personality Traits
<p>Dataset of <em>Prediction of Self-efficacy in Recognizing Deepfake based on HEXACO Personality Traits</em> </p>
Using social media and personality traits to assess software developers' emotions
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotions</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Nestor Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Personality traits and Pan I locus data of Atlantic cod juveniles
<p class="MsoNoSpacing"><span>Animals show among-individual variation in<span> </span>behaviours, including migration behaviours, which are often repeatable across time periods and contexts, commonly termed "personality". These behaviours can be correlated, forming a behavioural syndrome. </span><span>In this study, we assessed the repeatability and correlation of different behavioural traits i.e., boldness, exploration, and sociality and the link to feeding migration patterns in Atlantic cod juveniles. To do so, we collected repeated measurements within two short-term (three days) and two long-term (two months) intervals of these personality traits and genotypes of the <em>Pan </em>I locus, which is correlated to feeding migration patterns in this species. We found high repeatabilities for exploration behaviour in the short- and long-term intervals, and a trend for the relationship between exploration and the <em>Pan </em>I locus. Boldness and sociality were only repeatable in the second short-term interval indicating a possible development of stability over time and did not show a relation with the <em>Pan </em>I locus. We found no indication of behavioural syndromes among the studied traits. We were unable to identify the existence of a migration syndrome for the frontal genotype which is the reason that the link between personality and migration remains inconclusive, but we demonstrated a possible link between exploration and the <em>Pan</em> I genotype. This supports the need for further research that should focus on the effect of exploration tendency and other personality traits on cod movement, including the migratory (frontal) ecotype to develop management strategies based on behavioural units, rather than treating the population as a single homogeneous stock.</span></p>
Using social media and personality traits to assess software developers' emotional polarity
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotional polarity</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Short and long-term effects of endogenous cortisol on personality traits and behavioral syndromes
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Personality traits and Pan I locus data of Atlantic cod juveniles
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Data from: Personality traits change after an opportunity to mate
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