When phones get personal: Predicting Big Five personality traits from application usage

Peltonen, Ella; Sharmila, Parsa; Asare, Kennedy Opoku; Visuri, Aku; Lagerspetz, Eemil; Ferreira, Denzil

doi:10.1016/j.pmcj.2020.101269

Cited by 29 publications

(24 citation statements)

References 28 publications

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“…The data set used in this study was a subset of the large-scale crowdsourced Carat app data set from anonymous volunteers. The study data set was collected for a multifaceted purpose, which includes studying the relationship between smartphone app usage and Big 5 personality traits [ 40 ]; studying the similarities and differences in demographic, geographic, and cultural factors of smartphone usage [ 38 ]; and mental health research. The advertisement for the recruitment of participants was sent as push notifications through the Carat app to 25,323 verified users (ie, users with matching time zone and mobile country code) [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…All participants in this data set are Android-based smartphone users, who explicitly and voluntarily gave their consent from their mobile devices after they were informed about the purpose of the data collection, the data collection procedures, and management of the data set. The data set does not contain personally identifiable information, and was collected under the institutional review board license from the University of California, Berkeley and the University of Helsinki [ 21 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

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Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Asare¹,

Terhorst²,

Vega³

et al. 2021

JMIR Mhealth Uhealth

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Background Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. Objective The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. Methods Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. Results Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants’ age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status–normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status–normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression. Conclusions Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Asare¹,

Terhorst²,

Vega³

et al. 2021

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous works adopt mobile sensing approaches on modeling and recognizing contextual information, which not only serve applications such as health and physical activity monitoring [29][30][31][32], mental health monitoring [33][34][35], and aging care [36,37], but also benefit the research on understanding and predicting human individual behaviors and traits [19,[38][39][40][41].…”

Section: Previous Studies On Capturing Contextual Information Using Mobile Sensingmentioning

confidence: 99%

Putting human behavior predictability in context

et al. 2021

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Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shopping and online behaviors. However, the existing researches have been often limited to a single or to the combination of few behavioral dimensions, and they have adopted the perspective of an outside observer who is unaware of the motivations behind the specific behaviors or activities of a given individual. The key assumption of this work is that human behavior is deliberated based on an individual’s own perception of the situation that s/he is in, and that therefore it should also be studied under the same perspective. Taking inspiration from works in ubiquitous and context-aware computing, we investigate the role played by four contextual dimensions (or modalities), namely time, location, activity being carried out, and social ties, on the predictability of individuals’ behaviors, using a month of collected mobile phone sensor readings and self-reported annotations about these contextual modalities from more than two hundred study participants. Our analysis shows that any target modality (e.g. location) becomes substantially more predictable when information about the other modalities (time, activity, social ties) is made available. Multi-modality turns out to be in some sense fundamental, as some values (e.g. specific activities like “shopping”) are nearly impossible to guess correctly unless the other modalities are known. Subjectivity also has a substantial impact on predictability. A location recognition experiment suggests that subjective location annotations convey more information about activity and social ties than objective information derived from GPS measurements. We conclude the paper by analyzing how the identified contextual modalities allow to compute the diversity of personal behavior, where we show that individuals are more easily identified by rarer, rather than frequent, context annotations. These results offer support in favor of developing innovative computational models of human behaviors enriched by a characterization of the context of a given behavior.

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“…Bano, Shah e Ali (2019) destacam que a UTAUT foi estudada e analisada em várias áreas como educação, bancos, saúde, turismo e serviços de governo eletrônico e, recentemente, é considerada em estudos da personalidade. Corroborando, Peltonen et al (2020) ressaltam que os traços de personalidade dos indivíduos podem afetar o comportamento individual de uso de aplicativos e mídias sociais. Wang et al (2012) constataram que pessoas com diferentes traços de personalidade utilizam características diferentes de sites de redes sociais, por exemplo, as pessoas extrovertidas e com altos escores de Amabilidade tendem a fazer mais comentários nos perfis de outras pessoas e os neuróticos tendem a atualizar seu status como uma forma de autoexpressão.…”

Section: 2unclassified

Você foi Adicionado! A Participação em Grupos e a Intenção de Uso do WhatsApp a partir dos Fatores da Personalidade

Radons

Visentini

Löbler

2021

SCG

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Os traços de personalidade determinam os comportamentos de um indivíduo em diferentes aspectos da vida, incluindo sua intenção e uso de tecnologias. Assim, este artigo teve como objetivo verificar as relações entre os fatores da personalidade do indivíduo e sua intenção de uso do WhatsApp e a participação em grupos neste aplicativo. Realizou-se uma pesquisa survey, com 401 usuários do aplicativo. Foram utilizados três modelos teóricos, o Inventário dos Cinco Grandes Fatores da Personalidade (CGF) que foi validado no Brasil por Andrade (2008); o constructo Intenção Comportamental de Uso, da Teoria Unificada de Aceitação e Uso da Tecnologia (Unified Theory of Acceptance) – UTAUT, desenvolvido por Venkatesh et al. (2003); e o nível de participação em grupos, baseado em Bronstein et al. (2016). A análise dos resultados demonstrou que, das onze hipóteses propostas, cinco foram suportadas, indicando que pessoas extrovertidas e com altos níveis de Neuroticismo possuem intenção de usar o WhatsApp. Também foi constatado que os indivíduos que apresentam altos escores na dimensão Abertura e que são extrovertidos tendem a participar de grupos nessa rede social. Por fim, verificou-se que a intenção de uso do WhatsApp afeta positivamente a participação em grupos no aplicativo. Comprovou-se, assim, que as pessoas possuem motivações e interesses em relação às redes sociais virtuais de acordo com seus traços de personalidade, o que implica em considerar diferentes estratégias de comunicação e plataformas digitais para tornar o canal online uma ferramenta eficaz de comunicação e interação.

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When phones get personal: Predicting Big Five personality traits from application usage

Cited by 29 publications

References 28 publications

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Putting human behavior predictability in context

Você foi Adicionado! A Participação em Grupos e a Intenção de Uso do WhatsApp a partir dos Fatores da Personalidade

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