Detecting Low Self-Esteem in Youths from Web Search Data

Zaman, Anis; Acharyya, Rupam; Kautz, Henry; Silenzio, Vincent

doi:10.1145/3308558.3313557

Cited by 21 publications

(22 citation statements)

References 42 publications

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“…Mobile sensor data such as GPS logs [41,42]; electrodermal activity; and sleep behavior, motion, and phone use patterns [43,44] have been applied in investigating depressive symptoms. Zaman et al [45] found that individual private Google Search histories can be used to detect low self-esteem conditions among college students. Huckins et al [5] examined the longitudinal changes in mental health and smartphone use through ecological momentary assessments during COVID-19 among college populations.…”

Section: Prior Workmentioning

confidence: 99%

“…For online data and survey response storage, we used a Health Insurance Portability and Accountability Act-compliant cloud-based secure storing pipeline. The whole study design, pipelines, and survey measurements involved were similar to our previous setup [45] and have been approved by the Institutional Review Board of the University of Rochester.…”

Section: Recruitment and Study Designmentioning

confidence: 99%

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The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study

Zhang¹,

Zaman²,

Silenzio³

et al. 2020

JMIR Ment Health

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Background Depression and anxiety disorders among the global population have worsened during the COVID-19 pandemic. Yet, current methods for screening these two issues rely on in-person interviews, which can be expensive, time-consuming, and blocked by social stigma and quarantines. Meanwhile, how individuals engage with online platforms such as Google Search and YouTube has undergone drastic shifts due to COVID-19 and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in depression and anxiety profiles of users in a noninvasive manner. Objective The goal of this study is to examine, among college students in the United States, the relationships of deteriorating depression and anxiety conditions with the changes in user behaviors when engaging with Google Search and YouTube during COVID-19. Methods This study recruited a cohort of undergraduate students (N=49) from a US college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. The anxiety level was assessed via the General Anxiety Disorder-7 (GAD-7). The depression level was assessed via the Patient Health Questionnaire-9 (PHQ-9). This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data of all participants were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 features that can quantify shifts in online behaviors during the pandemic. We then assessed the correlations of deteriorating depression and anxiety profiles with each of these features. We finally demonstrated the feasibility of using the proposed features to build predictive machine learning models. Results Of the 49 participants, 49% (n=24) of them reported an increase in the PHQ-9 depression scores; 53% (n=26) of them reported an increase in the GAD-7 anxiety scores. The results showed that a number of online behavior features were significantly correlated with deteriorations in the PHQ-9 scores (r ranging between –0.37 and 0.75, all P values less than or equal to .03) and the GAD-7 scores (r ranging between –0.47 and 0.74, all P values less than or equal to .03). Simple machine learning models were shown to be useful in predicting the change in anxiety and depression scores (mean squared error ranging between 2.37 and 4.22, R2 ranging between 0.68 and 0.84) with the proposed features. Conclusions The results suggested that deteriorating depression and anxiety conditions have strong correlations with behavioral changes in Google Search and YouTube use during the COVID-19 pandemic. Though further studies are required, our results demonstrate the feasibility of using pervasive online data to establish noninvasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.

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Section: Prior Workmentioning

confidence: 99%

Section: Recruitment and Study Designmentioning

confidence: 99%

The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study

Zhang¹,

Zaman²,

Silenzio³

et al. 2020

JMIR Ment Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…For online data and survey response storage, we utilized a HIPAA- compliant cloud-based secure storing pipeline. The whole study design, pipelines, and survey measurements involved were similar to our previous setup in [ 43 ] and have been approved by the Institutional Review Board (IRB) of the University of Rochester.…”

Section: Methodsmentioning

confidence: 99%

“…Mobile sensor data, such as GPS logs [ 39,40 ], electrodermal activity, sleep behavior, motion, and phone usage patterns [ 41,42 ] has been applied in investigating depressive symptoms. [ 43 ] found that individual private Google Search histories can be used to detect low self-esteem conditions among college students. [ 5 ] examined the longitudinal changes in mental health and smartphone usages through ecological momentary assessments (EMAs) during COVID-19 among college populations.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study

Zaman

Zhang

Hoque

et al. 2020

Preprint

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Background: Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). Yet, current methods for screening mental health issues rely on in-person interviews, which can be expensive, time-consuming, blocked by social stigmas and quarantines. Meanwhile, how individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to COVID-19 and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in mental health profiles of users through a non-invasive manner. Objective: The goal of this study is to examine, among college students in the United States, the relationship between deteriorating mental health conditions and changes in user behaviors when engaging with Google Search and YouTube during COVID-19. Methods: This study recruited a cohort of undergraduate students (N=49) from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. The anxiety level was assessed via the General Anxiety Disorder-7 (GAD-7). The depression level was assessed via the Patient Health Questionnaire-9 (PHQ-9). This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data of all participants were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 signals that can quantify shifts in online behaviors during the pandemic. We then assessed the differences between groups with and without deteriorating mental health profiles in terms of these features. Results: Of the 49 participants, 41% (n=20) of them reported a significant increase (increase in the PHQ-9 score > 5) in depression, denoted as DEP; 45% (n=22) of them reported a significant increase (increase in the GAD-7 score > 5) in anxiety, denoted as ANX. Of the 5 features proposed to quantify online behavior changes, statistical significances were found between the DEP and non-DEP groups for all of them (P<.01, effect sizes \eta_{partial}^2 ranging between 0.130 to 0.320); statistical significances were found between the ANX and non-ANX groups for 4 of them (P<.02, effect sizes \eta_{partial}^2 ranging between 0.115 to 0.231). Significant features included late-night online activities, continuous usages and time away from the internet, porn consumptions, and keywords associated with negative emotions, social activities, and personal affairs. Conclusions: The results suggested strong discrepancies between college student groups with and without deteriorating mental health conditions in terms of behavioral changes in Google Search and YouTube usages during the COVID-19. Though further studies are required, our results demonstrated the feasibility of utilizing pervasive online data to establish non-invasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.

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“…Similar methods have been employed successfully in psychiatry using digital data extracted from social media sites [ 26 - 33 ]. However, few studies to date have explored the use of computational approaches to detect search patterns associated with psychiatric disorders [ 34 ]. Furthermore, while promising, online activity research thus far has been limited by the fact that it has been conducted primarily utilizing data extracted from anonymous individuals online who self-disclose having a particular diagnosis [ 35 ], and has yet to be carried out in real-world clinical settings using participant-contributed search data with clinically validated diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

Birnbaum¹,

Kulkarni²,

Meter³

et al. 2020

JMIR Ment Health

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Background Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

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Detecting Low Self-Esteem in Youths from Web Search Data

Cited by 21 publications

References 42 publications

The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study

The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study

The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

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