Twenty seconds of visual behaviour on social media gives insight into personality

Woods, Callum; Luo, Zhiyuan; Watling, Dawn; Durant, Szonya

doi:10.1038/s41598-022-05095-0

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…This differences may have led to differences in social cues processing and social strategies that were previously shown to be closely linked to personality (28). This might explain why Openness, Neuroticism and Agreeableness were successfully predicted 5-8 times in the current study and in the experiments with recommendation interfaces (38), while they did not arise in the experiment with social network browsing task, with no direct interactions with other people (39).…”

Section: Discussionmentioning

confidence: 59%

“…assessment element at the end). Laboratory studies showed prediction accuracy of .40 -.70 for Conscientiousness using such tasks as information seeking (19), social network browsing (39) and decision making (38).…”

Section: Discussionmentioning

confidence: 99%

“…We also conducted additional analysis to test whether the accuracy of prediction of three personality trait differs as per trait level (low, medium and high) following approach from (39).…”

Section: Classification By Low/medium/high Range Of the Traitsmentioning

confidence: 99%

“…Previous studies that linked eye movements and personality studies have mainly focused on the Five-Factor personality model (10,(17)(18)(19)21,26,38,39), curiosity (20,40), BIS/BAS (8,10,41) or optimism (42), with only a few studies examining other traits, such as the Dark Triad (8,43,44). Furthermore, most studies used a restricted number of gaze features such as dwelling time, number of fixations, and fixation duration (10,41,43), missing more subtle gaze features such as blink rate.…”

Section: Introductionmentioning

confidence: 99%

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AI can see you: Machiavellianism and Extraversion are reflected in eye-movements

Tsigeman,

Zemliak,

Likhanov

et al. 2023

Preprint

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Recent studies showed an association between personality traits and individual patterns of visual behaviour in laboratory and other settings. The current study extends previous research by measuring multiple personality traits in natural settings; and by comparing accuracy of prediction of multiple machine learning algorithms. Adolescent participants (N=35) completed personality questionnaires (Big Five Inventory and Short Dark Triad Questionnaire) and visited an interactive museum while their eye movements were recorded with head-mounted eye tracking. To predict personality traits the eye-movement data was analysed using eight machine-learning methods: Random forest, Adaboost, Naive Bayes, Support Vector Machine, Logistic Regression, k Nearest Neighbours, Decision Tree and a three-layer Perceptron.Extracted eye movement features introduced to machine learning algorithms predicted personality traits with above 33% chance accuracy (34% - 48%). This result is comparable to previous ecologically valid studies, but lower than in laboratory-based research. Better prediction was achieved for Machiavellianism and Extraversion compared to other traits (10 and 9 predictions above the chance level by different algorithms from different parts of the recording). Conscientiousness, Narcissism and Psychopathy were not reliably predicted from eye movements. These differences in predictability across traits might be explained by differential activation of different traits in different situations, such as new vs. familiar, exciting vs. boring, and complex vs. simple settings. In turn, different machine learning approaches seem to be better at capturing specific gaze patterns (e.g. saccades), associated with specific traits evoked by the situation. Further research is needed to gain better insights into trait-situation-algorithm interactions.

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Section: Discussionmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

“…We also conducted additional analysis to test whether the accuracy of prediction of three personality trait differs as per trait level (low, medium and high) following approach from (39).…”

Section: Classification By Low/medium/high Range Of the Traitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AI can see you: Machiavellianism and Extraversion are reflected in eye-movements

Tsigeman,

Zemliak,

Likhanov

et al. 2023

Preprint

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“…They revealed that the visual behaviour of individuals engaged in an everyday task can predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, and conscientiousness) as well as perceptual curiosity. Building on Hoppe’s work, Woods et al, ( 57 ) demonstrated that using just twenty seconds of visual behaviour on social-media gives insight into personality traits.…”

Section: Introductionmentioning

confidence: 99%

Assessment of cognitive biases in augmented reality: Beyond eye tracking.

Słowiński

Grindley

Muncie³

et al. 2022

JEMR

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We study an individual’s propensity for rational thinking; the avoidance of cognitive biases (unconscious errors generated by our mental simplification methods) using a novel augmented reality (AR) platform. Specifically, we developed an odd-one-out (OOO) game-like task in AR designed to try to induce and assess confirmatory biases. Forty students completed the AR task in the laboratory, and the short form of the comprehensive assessment of rational thinking (CART) online via the Qualtrics platform. We demonstrate that behavioural markers (based on eye, hand and head movements) can be associated (linear regression) with the short CART score – more rational thinkers have slower head and hand movements and faster gaze movements in the second more ambiguous round of the OOO task. Furthermore, short CART scores can be associated with the change in behaviour between two rounds of the OOO task (one less and one more ambiguous) – hand-eye-head coordination patterns of the more rational thinkers are more consistent in the two rounds. Overall, we demonstrate the benefits of augmenting eye-tracking recordings with additional data modalities when trying to understand complicated behaviours.

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Speech-based personality prediction using deep learning with acoustic and linguistic embeddings

Lukac

2024

Sci Rep

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This study introduces a novel method for predicting the Big Five personality traits through the analysis of speech samples, advancing the field of computational personality assessment. We collected data from 2045 participants who completed a self-reported Big Five personality questionnaire and provided free-form speech samples by introducing themselves without constraints on content. Using pre-trained convolutional neural networks and transformer-based models, we extracted embeddings representing both acoustic features (e.g., tone, pitch, rhythm) and linguistic content from the speech samples. These embeddings were combined and input into gradient boosted tree models to predict personality traits. Our results indicate that personality traits can be effectively predicted from speech, with correlation coefficients between predicted scores and self-reported scores ranging from 0.26 (extraversion) to 0.39 (neuroticism), and from 0.39 to 0.60 for disattenuated correlations. Intraclass correlations show moderate to high consistency in our model’s predictions. This approach captures the subtle ways in which personality traits are expressed through both how people speak and what they say. Our findings underscore the potential of voice-based assessments as a complementary tool in psychological research, providing new insights into the connection between speech and personality.

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Twenty seconds of visual behaviour on social media gives insight into personality

Cited by 9 publications

References 34 publications

AI can see you: Machiavellianism and Extraversion are reflected in eye-movements

AI can see you: Machiavellianism and Extraversion are reflected in eye-movements

Assessment of cognitive biases in augmented reality: Beyond eye tracking.

Speech-based personality prediction using deep learning with acoustic and linguistic embeddings

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