4,130 research outputs found
More than a feeling: A unified view of stress measurement for population science.
Stress can influence health throughout the lifespan, yet there is little agreement about what types and aspects of stress matter most for human health and disease. This is in part because "stress" is not a monolithic concept but rather, an emergent process that involves interactions between individual and environmental factors, historical and current events, allostatic states, and psychological and physiological reactivity. Many of these processes alone have been labeled as "stress." Stress science would be further advanced if researchers adopted a common conceptual model that incorporates epidemiological, affective, and psychophysiological perspectives, with more precise language for describing stress measures. We articulate an integrative working model, highlighting how stressor exposures across the life course influence habitual responding and stress reactivity, and how health behaviors interact with stress. We offer a Stress Typology articulating timescales for stress measurement - acute, event-based, daily, and chronic - and more precise language for dimensions of stress measurement
Social media use and adolescentsâ self-esteem and appearance satisfaction: The moderating role of school motivation
Background: This large-scale investigation speaks to the growing concern associated with the use of social media on the psychological wellbeing of adolescents. The study explored time spent using social networking sites as a predictor of teenagersâ self-esteem and appearance satisfaction and the protective role that motivation in school might play. Method: The sample comprised 10,546 adolescents at age 11 and 14 years, from the UKâs Millennium Cohort Study. Multiple linear regression determined cross-sectional and longitudinal associations between use of social media and self-esteem and appearance satisfaction. Time spent using social networking sites significantly predicted teenagersâ self-esteem and appearance satisfaction levels. Results: A significant interaction emerged with school motivation and social networking in relation to appearance satisfaction, suggesting that school motivation may help to buffer the negative effects of online social networking. Conclusion: In response to the ongoing concerns around the increase in adolescents who struggle with difficulties relating to their mental health, the finger of blame is frequently pointed to screen-based methods of social communication. It is anticipated that present findings will prompt the development of new interventions that target time spent using online social networking sites, particularly among teenage girls, during this new era of COVID-19-induced social isolation
Internet addiction in adolescents: prevalence and risk factors
As new media are becoming daily fare, Internet addiction appears as a potential problem in adolescents. From the reported negative consequences, it appears that Internet addiction can have a variety of detrimental outcomes for young people that may require professional intervention. Researchers have now identified a number of activities and personality traits associated with Internet addiction. This study aimed to synthesise previous findings by (i) assessing the prevalence of potential Internet addiction in a large sample of adolescents, and (ii) investigating the interactions between personality traits and the usage of particular Internet applications as risk factors for Internet addiction. A total of 3,105 adolescents in the Netherlands filled out a self-report questionnaire including the Compulsive Internet Use Scale and the Quick Big Five Scale. Results indicate that 3.7% of the sample were classified as potentially being addicted to the Internet. The use of online gaming and social applications (online social networking sites and Twitter) increased the 2 risk for Internet addiction, whereas extraversion and conscientiousness appeared as protective factors in high frequency online gamers. The findings support the inclusion of 'Internet addiction' in the DSM-V. Vulnerability and resilience appear as significant aspects that require consideration in further studies
Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise
Social media based digital epidemiology has the potential to support faster
response and deeper understanding of public health related threats. This study
proposes a new framework to analyze unstructured health related textual data
via Twitter users' post (tweets) to characterize the negative health sentiments
and non-health related concerns in relations to the corpus of negative
sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the
collection of 6 million Tweets for one month, this study identified the
prominent topics of users as it relates to the negative sentiments. Our
proposed framework uses two text mining methods, sentiment analysis and topic
modeling, to discover negative topics. The negative sentiments of Twitter users
support the literature narratives and the many morbidity issues that are
associated with DDEO and the linkage between obesity and diabetes. The
framework offers a potential method to understand the publics' opinions and
sentiments regarding DDEO. More importantly, this research provides new
opportunities for computational social scientists, medical experts, and public
health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science
and Technology (ASIST
A systematic study on predicting depression using text analytics
Social Networking Sites (SNS) provides online communication among groups but somehow it is affecting the status of mental health. For adolescents with limited social media friends and using internet for communication purposes predicted less depression, whereas non-communication desire reveals more depression and anxiety disorder. Social media posts and comments provide a rich source of text data for academic research. In this paper, we have discussed various text analytical approaches to predict depression among users through the sharing of online ideas over such websites. This paper presents a comprehensive review for predicting depression disorder by various text analytics approaches. This paper also presents the summary of results obtained by some researchers available in literature to predict MajorDepressive Disorder (MDD). In future research, enable self-monitoring of health status of each individuals which may help to increase well-being of an identity.Keywords: Social Networking Sites; Sentiment Analysis; Machine Learning; Support Vector Machine
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