Sometimes it’s a bit of a strange situation that you find a new piece of research by someone you know through someone you know. I found this umbrella review by Patti Valkenburg via Dan Willingham. It’s a small world indeed.

What is this preprint about?

Research into the impact of social media use (SMU) on well-being (e.g., happiness) and ill- being (e.g., depression) has exploded over the past few years. From 2019 to August 2021, 27 reviews have been published: nine meta-analyses, nine systematic reviews, and nine narrative reviews, which together included hundreds of empirical studies. The aim of this umbrella review is to synthesize the results of these meta-analyses and reviews. Even though the meta- analyses are supposed to rely on the same evidence base, they yielded disagreeing associations with well- and ill-being, especially for time spent on SM, active SMU, and passive SMU. This umbrella review explains why their results disagree, summarizes the gaps in the literature, and ends with recommendations for future research.

And the conclusion is pretty clear:

The nine meta-analyses in this umbrella review disagreed in their conclusions about the associations of different types of SMU with well-being. This particularly applied to the time-based predictors and not or less to the other predictors. However, despite these inconsistencies, all meta-analyses yielded associations that were mostly small (for the time- based predictors), occasionally moderate (for problematic SMU), but never large. The conclusions of the meta-analyses were largely supported by the narrative and systematic reviews, which observed comparable gaps in the literature and provided comparable suggestions for future research.

But Valkenburg also makes some good recommendations:

• Don’t collapse across well- and ill-being outcomes
• We need content-based SM predictors of well-/ill-being
• We need a causal effect heterogeneity paradigm

The last one is maybe harder to understand, it means:

What is still too often overlooked in these studies is that such average associations are derived from heterogeneous populations of SM users who differ in how they select and respond to SM [57], a finding that has repeatedly been confirmed in qualitative studies [58]. To truly understand the effects of SMU, researchers need to take the next step, that is, adopting a “causal effect heterogeneity” approach [59, 60], which enables them to better understand why and how individuals differ in their responses to SMU.