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Predicting personality traits from touchscreen based interactions

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... Recent trends in APP have led to a proliferation of studies through analyzing different data types, in particular analyzing speech [4][5][6], image [7][8][9], video [10][11][12], , social media contents and activities [13][14][15], emails [16], handwriting [17,18], touch screen-based interaction [19], signature [20], nonverbal behaviors [21], mobile short message services (SMS) [22], and so on. Along with this growth in APP, however there is an increasing concern over the accuracy of the predictions. ...
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Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.
... Recent trends and advancements in the personality prediction field guided the exploration of various types of possible data available in social media platforms that can be employed to build automatic prediction models. Multiple experiments utilized several types of data ranging from social network contents and activities to ordinary textual inputs from posts/tweets [52] [25], likes history [18] [17], profile picture choice [84] [19], nonverbal behaviors [105], touchscreen-based interaction [106]. As manifested by Kosinski et al. in [16], the final trained personality prediction models from Like history were more accurate than assessments made by users' friends or family members. ...
... In a next step, we will build a predictive model for personality traits and depression level based on our collected smartphone data. Recently, personality was successfully predicted based on touchscreen-based interactions [37] and smartphone accelerometer data [23]. ...
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Knowledge of users’ affective states can improve their interaction with smartphones by providing more personalized experiences (e.g., search results and news articles). We present an affective state classification model based on data gathered on smartphones in real-world environments. From touch events during keystrokes and the signals from the inertial sensors, we extracted two-dimensional heat maps as input into a convolutional neural network to predict the affective states of smartphone users. For evaluation, we conducted a data collection in the wild with 82 participants over 10 weeks. Our model accurately predicts three levels (low, medium, high) of valence (AUC up to 0.83), arousal (AUC up to 0.85), and dominance (AUC up to 0.84). We also show that using the inertial sensor data alone, our model achieves a similar performance (AUC up to 0.83), making our approach less privacy-invasive. By personalizing our model to the user, we show that performance increases by an additional 0.07 AUC.
... With the increasing variety of data types available for analysing the personality of people, aspects of view to APP increases likewise. In this point of view to the assortment of APP, data types can be named as: speech [3][4][5][6], image [7][8][9][10], video [11,12], text [13][14][15], social media activities [16][17][18], touch screen interaction [19,20], and so on. Also, each of these has subsets and divisions of text-based APP which can be mentioned are email [21], SMS [22], and tweets & posts on social media [23]. ...
Preprint
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Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.
... Recent trends in APP have led to a proliferation of studies through analyzing different data types, in particular analyzing speech [4][5][6], image [7][8][9], video [10][11][12], , social media contents and activities [13][14][15], emails [16], handwriting [17,18], touch screen-based interaction [19], signature [20], nonverbal behaviors [21], mobile short message services (SMS) [22], and so on. Along with this growth in APP, however there is an increasing concern over the accuracy of the predictions. ...
Preprint
Full-text available
Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.
... Note that information on the relevant individual differences can be collected upfront using the standard tests we used in the study, after which personalization can be enable by setting a related parameter in the ACSP. However, we can also explore the option of predicting these values in realtime from interaction data as students work with the ACSP, as it has been done, for instance, [24]. ...
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This chapter presents reanalyses of data originally reported in McCrae (2001) in an enlarged sample of cultures. Analyses of age and gender differences, the generalizability of culture profiles across gender and age groups, and culture-level factor structure and correlates are replicated after the addition of 30 new subsamples from 10 cultures. Cross-cultural variations in the standard deviations of NEO-PI-R scales are also examined. Standardized factor- and facet-level means are provided for use by other researchers.
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We show how users' activity on Facebook relates to their personality, as measured by the standard Five Factor Model. Our dataset consists of the personality profiles and Facebook profile data of 180,000 users. We examine correlations between users' personality and the properties of their Facebook profiles such as the size and density of their friendship network, number uploaded photos, number of events attended, number of group memberships, and number of times user has been tagged in photos. Our results show significant relationships between personality traits and various features of Facebook profiles. We then show how multivariate regression allows prediction of the personality traits of an individual user given their Facebook profile. The best accuracy of such predictions is achieved for Extraversion and Neuroticism, the lowest accuracy is obtained for Agreeableness, with Openness and Conscientiousness lying in the middle.
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One hundred and eleven individuals completed the NEO Five Factor Inventory along with three measures of subjective well-being, the Oxford Happiness Inventory, the Depression–Happiness Scale, and the Satisfaction With Life Scale. Regression analysis showed that although Extraversion and Neuroticism best predicted scores on the Oxford Happiness Inventory, it was Neuroticism and Conscientiousness that best predicted scores on the Satisfaction with Life Scale. These results provide further evidence that Neuroticism and Extraversion are dimensions of personality related to subjective well-being. However, the results also suggest that Conscientiousness is an additional dimension of personality relevant to understanding subjective well-being.
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This study examined the relationships of the traits associated with Gray's behavioural approach system (BAS) and behavioural inhibition system (BIS) with cognitive processing of emotional information. Initially, participants completed questionnaires covering trait impulsivity and anxiety, and BAS and BIS sensitivities. They were then tested individually. After completing a questionnaire of current positive and negative moods, they completed three tasks measuring processing of pleasant, unpleasant and neutral information. Consistent with Gray’s theory, the results showed that impulsivity and BAS sensitivity were associated with the processing of pleasant information, while anxiety and BIS sensitivity were associated with the processing of unpleasant information. These findings imply that Gray’s BAS–BIS theory can be extended to cognitive processing of emotional information.
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The present study was designed to investigate extraversion-related individual differences in the speed of transmission of sensory input into motor output. In a sample of 16 introverted and 16 extraverted female volunteers, event-related potentials, lateralized readiness potentials (LRPs), and electromyogram (EMG) were recorded as participants performed a visual choice reaction time task. As additional behavioral indicators of performance, measures of reaction time (RT) and response dynamics were obtained. Although extraversion-related differences were found neither for behavioral measures nor for the N1 and P3 components of the evoked potential, introverts showed a reliably shorter latency in stimulus-locked LRP than extraverts. This latter finding supports the notion of faster stimulus analysis in introverts compared to extraverts. Furthermore, there was no indication of extraversion-related individual differences in speed of response organization and response execution as indicated by response-locked LRP and EMG latencies, respectively. However, a significantly higher EMG amplitude observed with introverts pointed to a less accurately adjusted motor output system of introverts compared to extraverts.
The Cambridge handbook of personality psychology
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Pflegetab: Enhancing quality of life using a psychosocial internet-based intervention for residential dementia care
  • J.-N Antons
  • J O'sullivan
  • S Arndt
  • P Gellert
  • J Nordheim
  • S Möller
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J.-N. Antons, J. O'Sullivan, S. Arndt, P. Gellert, J. Nordheim, S. Möller, and A. Kuhlmey, "Pflegetab: Enhancing quality of life using a psychosocial internet-based intervention for residential dementia care," in Proceedings of International Society for Research on Internet Interventions. Sanford NC, USA: International Society for Research on Internet Interventions, 2016, pp. 1-1, online.
Pflegetab: Enhancing quality of life using a psychosocial internet-based intervention for residential dementia care
  • J.-N Antons
  • J Sullivan
  • S Arndt
  • P Gellert
  • J Nordheim
  • S Möller