The mental health and well-being profile of young adults using social media

From temperament to YouTube: exploring the link between childhood temperament, YouTube usage patterns, and emotional/behavioral problems among children

BACKGROUND

YouTube is a widely used video sharing and social networking platform among children and adolescents. However, research on YouTube usage among this population remains scarce. Specifically, studies on factors that influence children and adolescents' usage are clinically significant but largely lacking. Additionally, few studies have examined the association between usage and emotional/behavioral problems, which is fundamental to smartphone research. Therefore, this study explored the relationship between early childhood temperament, subsequent YouTube usage patterns, and emotional/behavioral problems.

METHODS

The Kids Cohort for Understanding Internet Addiction Risk Factors in Early Childhood (K-CURE) is the first long-term prospective cohort study in Korea aimed at understanding the long-term effects of media exposure on young children. The study included 195 children aged 8-11 years enrolled in the K-CURE study. Caregivers, predominantly mothers, who voluntarily participated during their visits to community centers for children's mental health in Korea's major cities, completed a detailed self-administered survey. Childhood temperament was measured in 2018 when the children were 5-8 years old. Subsequent YouTube usage patterns and emotional/behavioral problems were assessed in 2021. Data were analyzed using frequency analysis, correlation analysis, and multiple linear regression.

RESULTS

The study found that 21.0% of children started using YouTube before age 4, with the most common onset age being 8-9 years (30.3%). These children used YouTube on average 4.8 days per week for 68.5 min per day. Early childhood persistence was negatively associated with the subsequent YouTube usage duration, and the age at first YouTube use was negatively correlated with subsequent usage frequency. Furthermore, a younger age at first YouTube use and higher usage frequency were significantly associated with increased emotional/behavioral problems.

CONCLUSIONS

In the YouTube environment, where content is automatically recommended based on user preferences, traits related to usage patterns may be associated with persistence, which is linked to self-regulation. Considering the current trend where children use smartphones, contents frequently for very short durations, our findings highlight the importance of self-regulation in the media usage of children who are still developing. Additionally, our results provide fundamental information for future YouTube studies and illustrate similarities and differences between smartphone and YouTube research.

Identifying protective and risk behavior patterns of online communication in young people

INTRODUCTION

Research has investigated the association between time spent online and mental well-being, however the nuances between specific online behaviors and well-being have been less explored. This research examines how specific online behaviors (i.e., how young people are engaging online and with whom), are associated with one another, and how these patterns of behaviors are related to well-being.

METHODS

We used the November 2020 and March 2021 Understanding Society COVID-19 Panel data. The sample consisted of 1432 adolescents aged 10-15 years, who participated in November 2020. Latent class analysis was used to explore patterns of online behaviors. We also investigated how sociodemographic characteristics differed across the classes, along with physical, social, and mental well-being as distal outcomes both cross-sectionally and longitudinally.

RESULTS

We identified four classes: "Avid users," "Scholars," "Midways," and the "Passengers." The avid users had the highest frequency of posting online content regularly, likewise the scholars also posted online content regularly, however the scholars were differentiated by their higher frequency of schoolwork and news intake online. The midways had more complex activity characterized by talking to friends often and having a social media account, but posted online content less frequently. The passengers were the least active online as they posted pictures and videos less (76% said "never") and only 63% had a social media account. The avid users had the lowest well-being cross-sectionally and longitudinally, and the midways had lower social well-being and appearance dissatisfaction.

CONCLUSIONS

Online behaviors such as regularly posting or talking to internet-only friends could be related to lower well-being. Policymakers should consider both improving regulations online and building an evidence base to enable caregivers from all backgrounds to support young people.

Methodologies for Monitoring Mental Health on Twitter: Systematic Review

BACKGROUND

The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results.

OBJECTIVE

This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used.

METHODS

A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations.

RESULTS

The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention.

CONCLUSIONS

The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.