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
- URL: http://arxiv.org/abs/2009.09076v1
- Date: Sat, 5 Sep 2020 00:54:57 GMT
- Title: 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
- Authors: Anis Zaman, Boyu Zhang, Ehsan Hoque, Vincent Silenzio, Henry Kautz
- Abstract summary: How individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to pandemic and subsequent lockdowns.
This study recruited a cohort of 49 students from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant.
From individual-level Google Search and YouTube histories, we developed 5 signals that can quantify shifts in online behaviors during the pandemic.
- Score: 1.74243547444997
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mental health problems among the global population are worsened during the
coronavirus disease (COVID-19). How individuals engage with online platforms
such as Google Search and YouTube undergoes drastic shifts due to pandemic 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 in a non-invasive manner. The goal of this study is
to examine, among college students, the relationship between deteriorating
mental health conditions and changes in user behaviors when engaging with
Google Search and YouTube during COVID-19. This study recruited a cohort of 49
students from a U.S. college campus during January 2020 (prior to the pandemic)
and measured the anxiety and depression levels of each participant. 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 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. 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. 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.
Related papers
- On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook [21.978924582262263]
Depression is the most widely studied mental health condition.
The COVID-19 global pandemic has had a great impact on mental health worldwide.
We present a survey on natural language processing (NLP) approaches to modeling depression in social media.
arXiv Detail & Related papers (2024-10-11T13:20:54Z) - Predicting Depression and Anxiety: A Multi-Layer Perceptron for
Analyzing the Mental Health Impact of COVID-19 [1.9809980686152868]
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends during the COVID-19 pandemic.
Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults.
This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health.
arXiv Detail & Related papers (2024-03-09T22:49:04Z) - Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths.
The pandemic has impacted and even changed human behavior in almost every aspect.
Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z) - Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms.
The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
arXiv Detail & Related papers (2023-02-05T22:33:49Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - Artificial Intelligence-Based Analytics for Impacts of COVID-19 and
Online Learning on College Students' Mental Health [0.0]
COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019.
The virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020.
This paper seeks to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing.
arXiv Detail & Related papers (2022-02-07T05:24:52Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - Social Behavior and Mental Health: A Snapshot Survey under COVID-19
Pandemic [6.5721468981020665]
COVID-19 pandemic has changed the way we live, study, socialize and recreate.
There are growing researches that leverage online social media analysis to detect and assess user's mental status.
arXiv Detail & Related papers (2021-05-17T21:08:03Z) - Deep Multi-task Learning for Depression Detection and Prediction in
Longitudinal Data [50.02223091927777]
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally.
Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment.
We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
arXiv Detail & Related papers (2020-12-05T05:14:14Z) - Identifying pandemic-related stress factors from social-media posts --
effects on students and young-adults [2.198430261120653]
The COVID-19 pandemic has thrown natural life out of gear across the globe.
Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation.
This has led to wide-spread gloom and depression across society but more so among the young and the elderly.
arXiv Detail & Related papers (2020-12-01T08:42:27Z) - Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
arXiv Detail & Related papers (2020-08-09T20:22:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.