Social Media and Youth Mental Health: Simple Narratives Produce Biased Interpretations

• Published in: Journal of psychopathology and clinical science 2024-10, Vol.133 (7), p.507-514
• Main Authors: Sewall, Craig J. R., Parry, Douglas A.
• Format: Article
• Language: English
• Subjects: Academic staff; Adolescent Development; Adolescent Health; Bias; Causal models; Confounding factors; Etiology; Health problems; Human; Mental disorders; Mental health; Narratives; Psychological aspects; Side effects; Simulation; Social Media; Social Media Use; Specification; Youth; Youth Mental Health
• ISSN: 2769-7541; 2769-755X; 2769-755X
• DOI: 10.1037/abn0000950

Many academics and pundits contend that social media use is the primary cause of an international youth mental health crisis. However, these claims often rely on correlational evidence, ignoring the confounding effects of developmental, environmental, social, and psychological factors that influence mental health. This oversimplifies the complex etiology of mental health problems. We call for a more nuanced understanding of the role of social media in youth mental health that avoids oversimplification. Additionally, we urge researchers to move beyond vague, narrative-driven verbal theories and encode them into precise, testable causal models. Using simulation techniques and specification curve analyses, we show how misspecified models that ignore these confounding factors can lead to biased conclusions about social media's adverse effects. This simplistic focus on social media use diverts attention from the broader factors contributing to youth mental health problems, hindering the development of effective interventions and support. General Scientific Summary This article highlights the need for a more nuanced understanding of how social media impacts youth mental health. It shows that focusing solely on social media as the primary cause of mental health problems can lead to biased conclusions and overlook other significant factors. The research emphasizes the importance of using precise, testable models to account for these complexities, ensuring that interventions and policies are more effective.