You're gish-galloping a bunch of random studies, most of which appear to be cross-sectional. I've linked a broad review.

Even the longitudinal studies are poor here. See, for example, as this Nature article notes:

    "The study has multiple limitations that need to be considered. First, to interpret the parameters from our analyses as estimates of causal effects one would need to adopt the following assumptions: (a) there are no time-varying unobserved confounders that impact the relation between social media use and life satisfaction; (b) the model adequately accounts for unobserved time-invariant confounding through the inclusion of a random intercept; (c) there is no measurement error in the variables; (d) the time interval between studies (one year) is the right length to capture the effects of interest; and (e) the bidirectional links estimated by our longitudinal model are linear in nature. Only if these assumptions are met can this observational study be said to capture the causal effects between social media and life satisfaction. Second, the data are self-report and therefore only allow inferences about the impact of self-estimated time on social media, rather than objectively measured social media use." https://www.nature.com/articles/s41467-022-29296-3#Sec2

I'd also suggest looking at the coefficients (effect sizes) in the above (regression coefficients barely approaching 0.2), and other articles. The effects here, even if we were to pretend they were clearly established, are incredibly tiny. Examples:

- social media explaining only 0.4% of variance (https://pubmed.ncbi.nlm.nih.gov/30944443/)

- social-media/mental-health effect around β = .061 (https://christopherjferguson.com/Social%20Media%20Meta.pdf)

These are basically nothing, and yet you have such absolute confidence from people that social media is this big harmful thing. The evidence just isn't there.

Social media bans for teenagers lack evidence and pose risks, scientists say