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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2401.10305 |
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| _version_ | 1866929218484436992 |
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| author | Sze, Wun Yung Shaney Herrero, Maryglen Pearl Garriga, Roger |
| author_facet | Sze, Wun Yung Shaney Herrero, Maryglen Pearl Garriga, Roger |
| contents | This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we were able to predict users' personality up to a 0.78 F1 score on a two class problem. Given the fast growing number of data collected from mobile phones, our novel personality indicators open the door to exciting avenues for future research in social sciences. Our results reveal distinct behavioral patterns that proved to be differentially predictive of big five personality traits. They potentially enable cost effective, questionnaire free investigation of personality related questions at an unprecedented scale. We show how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality. These findings have practical implications for organizations harnessing mobile sensor data for personality assessment, guiding the refinement of more precise and efficient prediction models in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10305 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personality Trait Inference Via Mobile Phone Sensors: A Machine Learning Approach Sze, Wun Yung Shaney Herrero, Maryglen Pearl Garriga, Roger Signal Processing Machine Learning This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we were able to predict users' personality up to a 0.78 F1 score on a two class problem. Given the fast growing number of data collected from mobile phones, our novel personality indicators open the door to exciting avenues for future research in social sciences. Our results reveal distinct behavioral patterns that proved to be differentially predictive of big five personality traits. They potentially enable cost effective, questionnaire free investigation of personality related questions at an unprecedented scale. We show how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality. These findings have practical implications for organizations harnessing mobile sensor data for personality assessment, guiding the refinement of more precise and efficient prediction models in the future. |
| title | Personality Trait Inference Via Mobile Phone Sensors: A Machine Learning Approach |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.10305 |