The probability of predicting personality traits by the way user types on touch screen

Roy, Soumen; Roy, Utpal; Sinha, Devadatta

doi:10.1007/s11334-018-0317-6

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Activities on touch screens produce a series of timing features that have been successfully used in identifying traits [28], [333]. In the previous studies, only timing feature vectors have been analysed which provides insufficient feature arrangements in predicting traits because of multiple factors, such as a higher rate of intra-class variation [198].…”

Section: Improper Utilization Of Featuresmentioning

confidence: 99%

“…It is determined by the user's mental state (excited, angry, sad, or normal) and position (sitting, walking, standing, running, jogging, or laying) [337], [394], [395]. Due to this fact, a study [333] used a score-fusion method where scores of multiple classifiers were considered. However, advanced sensing features like gyroscopes, accelerometers, and rotation information are readily available, prominent, and hidden features created simultaneously with the timing features.…”

Section: Improper Utilization Of Featuresmentioning

confidence: 99%

“…It requires careful consideration in the machine learning (ML) model evaluation. Studies [333] applied (Leave-One-User-Out Cross Validation) LOUOCV to address this problem. However, the non-uniformity of KD raises the additional difficulty of class imbalance in LOUOCV.…”

Section: P Conventional Model Evaluation Test Optionmentioning

confidence: 99%

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A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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Section: Improper Utilization Of Featuresmentioning

confidence: 99%

Section: Improper Utilization Of Featuresmentioning

confidence: 99%

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A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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“…In addition to resolving data gaps, our study might have benefited from a greater diversity in data categories, e.g. Bluetooth logs, Wi-Fi, keystroke patterns, Internet logs -that some previous studies [9,47,50] utilized to predict personality. However, we expect that the addition of these data categories would bring only marginal improvements, given that our models outperform the aforementioned studies.…”

Section: Limitationsmentioning

confidence: 99%

Modeling Personality vs. Modeling Personalidad

Khwaja

Vaid

Zannone

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Sensor data collected from smartphones provides the possibility to passively infer a user's personality traits. Such models can be used to enable technology personalization, while contributing to our substantive understanding of how human behavior manifests in daily life. A significant challenge in personality modeling involves improving the accuracy of personality inferences, however, research has yet to assess and consider the cultural impact of users' country of residence on model replicability. We collected mobile sensing data and self-reported Big Five traits from 166 participants (54 women and 112 men) recruited in five different countries (UK, Spain, Colombia, Peru, and Chile) for 3 weeks. We developed machine learning based personality models using culturally diverse datasets -representing different countries -and we show that such models can achieve state-of-the-art accuracy when tested in new countries, ranging from 63% (Agreeableness) to 71% (Extraversion) of classification accuracy. Our results indicate that using country-specific datasets can improve the classification accuracy between 3% and 7% for Extraversion, Agreeableness, and Conscientiousness. We show that these findings hold regardless of gender and age balance in the dataset. Interestingly, using gender-or age-balanced datasets as well as gender-separated datasets improve trait prediction by up to 17%. We unpack differences in personality models across the five countries, highlight the most predictive data categories (location, noise, unlocks, accelerometer), and provide takeaways to technologists and social scientists interested in passive personality assessment.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Text-based automatic personality prediction: A bibliographic review

Feizi-Derakhshi,

Ramezani

et al. 2021

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; pretrained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.

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The probability of predicting personality traits by the way user types on touch screen

Cited by 8 publications

References 10 publications

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Modeling Personality vs. Modeling Personalidad

Text-based automatic personality prediction: A bibliographic review

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