Effect of Social Support on Mental Health: The Study of Social Networks in Times of Crisis
DOI:
https://doi.org/10.63332/joph.v5i7.2899Keywords:
Social Support, Mental Health, Social Networks, Crisis Events, Anxiety and Depression, Machine Learning, Sentiment Analysis, Digital Support Systems, Emotional Well-being, Vulnerable PopulationsAbstract
Social support is crucial for mental and emotional well-being, growth, and lowering the risk of psychological discomfort. Young people have access to a variety of peer-socialization options, including those provided by social media. The impact of social support on mental health has garnered increasing attention, particularly during times of crisis when emotional resilience is most tested. Social networks both traditional and digital serve as critical channels for providing support, influencing individuals’ psychological well-being during events like pandemics, wars, and natural disasters. This review explores the dual nature of social networks in promoting mental health and contributing to psychological distress. The article delves into the many forms and origins of social support, explores theoretical frameworks like the buffering hypothesis and social support theory, and emphasizes the disproportionate impact of crises on vulnerable groups, such as children, women, and the elderly. Recent studies employing machine learning techniques such as logistic regression, decision trees, and sentiment analysis have been effective in identifying emotional trends and distress signals in online interactions. The evaluation also discusses how social media may help with mental health issues, including cyberbullying and social isolation, as well as how it can exacerbate these problems. Furthermore, the paper examines the limitations of current behavioral models and technological interventions while advocating for human-centered approaches in digital mental health solutions.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
CC Attribution-NonCommercial-NoDerivatives 4.0
The works in this journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
