Predicting anxiety using Google and Youtube digital traces
Anxiety is a widespread and serious mental health issue that has been exacerbated by the COVID-19 pandemic and other stressors. In this study, we explore how online behavior data from Google and YouTube can be used to infer anxiety levels in individuals. We collected and processed digital traces fro...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Emerging Trends in Drugs, Addictions, and Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667118224000047 |
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| author | Joshua Rochotte Aniket Sanap Vincent Silenzio Vivek K. Singh |
| author_facet | Joshua Rochotte Aniket Sanap Vincent Silenzio Vivek K. Singh |
| author_sort | Joshua Rochotte |
| collection | DOAJ |
| description | Anxiety is a widespread and serious mental health issue that has been exacerbated by the COVID-19 pandemic and other stressors. In this study, we explore how online behavior data from Google and YouTube can be used to infer anxiety levels in individuals. We collected and processed digital traces from nearly 100 participants over eight weeks and applied various machine learning techniques to extract features and build predictive models. We found that combining data from multiple media modalities can yield highly accurate predictive models for anxiety as self-reported by a clinical GAD-7 scale (AUC > 0.86). We also found that the semantic categories of online engagement can affect the predictive performance of the models. This study contributes to the field of computational social science and digital mental health and demonstrates the potential of using online behavior data to monitor psychological well-being and design interventions for anxiety. |
| format | Article |
| id | doaj-art-4d91a0fd19f84b20a55eabbe6ef82b41 |
| institution | OA Journals |
| issn | 2667-1182 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Emerging Trends in Drugs, Addictions, and Health |
| spelling | doaj-art-4d91a0fd19f84b20a55eabbe6ef82b412025-08-20T01:56:34ZengElsevierEmerging Trends in Drugs, Addictions, and Health2667-11822024-12-01410014510.1016/j.etdah.2024.100145Predicting anxiety using Google and Youtube digital tracesJoshua Rochotte0Aniket Sanap1Vincent Silenzio2Vivek K. Singh3School of Communication & Information, Rutgers University, NJ, USA; Corresponding author.Department of Computer Science, Rutgers University, New Brunswick, NJ, USASchool of Public Health, Rutgers University, New Brunswick, NJ, USASchool of Communication & Information, Rutgers University, NJ, USAAnxiety is a widespread and serious mental health issue that has been exacerbated by the COVID-19 pandemic and other stressors. In this study, we explore how online behavior data from Google and YouTube can be used to infer anxiety levels in individuals. We collected and processed digital traces from nearly 100 participants over eight weeks and applied various machine learning techniques to extract features and build predictive models. We found that combining data from multiple media modalities can yield highly accurate predictive models for anxiety as self-reported by a clinical GAD-7 scale (AUC > 0.86). We also found that the semantic categories of online engagement can affect the predictive performance of the models. This study contributes to the field of computational social science and digital mental health and demonstrates the potential of using online behavior data to monitor psychological well-being and design interventions for anxiety.http://www.sciencedirect.com/science/article/pii/S2667118224000047AnxietyMachine learningGoogleYouTubeDigital traces |
| spellingShingle | Joshua Rochotte Aniket Sanap Vincent Silenzio Vivek K. Singh Predicting anxiety using Google and Youtube digital traces Emerging Trends in Drugs, Addictions, and Health Anxiety Machine learning YouTube Digital traces |
| title | Predicting anxiety using Google and Youtube digital traces |
| title_full | Predicting anxiety using Google and Youtube digital traces |
| title_fullStr | Predicting anxiety using Google and Youtube digital traces |
| title_full_unstemmed | Predicting anxiety using Google and Youtube digital traces |
| title_short | Predicting anxiety using Google and Youtube digital traces |
| title_sort | predicting anxiety using google and youtube digital traces |
| topic | Anxiety Machine learning YouTube Digital traces |
| url | http://www.sciencedirect.com/science/article/pii/S2667118224000047 |
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