How do social media recommendation algorithms affect mental health?

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1. 1

   The Dark Side of Social Media: Recommender Algorithms and Mental Health

   CESifo (Mandile)Published May 7, 2026Checked Oct 16, 2026

   “This paper investigates the impact of social media algorithms on mental health outcomes. I exploit a quasi-experimental setting combining data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) panel and the introduction of the algorithmic feed on Instagram in 2016. I estimate a difference-in-differences model comparing individuals with an Instagram account to individuals with accounts on other social media platforms. The results show that the introduction of the algorithm on Instagram had a negative impact on teenagers’ mental health, affecting both males and females. Additional evidence on mechanisms reveals that the algorithm fosters social isolation and intensifies negative social comparison, particularly affecting females through increased need for validation and diminished self-worth and self-esteem.”

2. 2

   Social media and mental health: The role of algorithms and regulation

   Econstor (Econstor) (Makarin & Mandile)Published Jan 1, 2025Checked Oct 16, 2026

   “Causal research consistently shows - across platforms, methods, and time periods - that social media is harmful for mental health. Promising remedies include helping parents coordinate restrictions on children's use, for example, through school smartphone bans. Other proposals include disabling addictive features for broader segments of the population and enforcing greater data transparency. Regulators should nevertheless proceed with caution, since social media generates large consumer surplus, only partially explained by addictive behavior.”

3. 3

   Algorithmic recommendation and adolescent mental health: a grounded theory study of social and commercial determinants.

   BMC public health (Winstone et al.)Published Jun 24, 2026Checked Oct 16, 2026

   “Algorithms organised what content participants encountered, how frequently, and with what emotional intensity, within broader socioecological contexts. Three interlocking processes sustained exposure; algorithmic reinforcement of engagement, emotional feedback loops between mood and recommendations, and a persistent sense of limited control despite high awareness. Although participants developed adaptive strategies and algorithmic literacy, these were unevenly distributed and often insufficient within engagement-driven, profit-oriented systems.ConclusionsDigital mental health may be produced through interactions between individual capacities, social environments, and commercially driven platform design. Protecting population mental health therefore requires multi-level action, including developmentally appropriate social media and algorithmic literacy education alongside structural reforms to recommendation systems, transparency, and platform governance.”

4. 4

   The bias beneath: analyzing drift in YouTube’s algorithmic recommendations

   Social Network Analysis and Mining (Çakmak et al.)Published Aug 24, 2024Checked Oct 16, 2026

   “To uncover these patterns, we used a mixed methods approach to analyze videos recommended by YouTube. We looked at the emotions conveyed in videos, the moral messages they might carry, and whether they contained harmful content. Our research also involved statistical analysis to detect biases in how these videos are recommended and network analysis to see how certain videos become more influential than others. Our findings show that YouTube’s algorithms can lead to a narrowing of the content landscape, limiting the diversity of what gets recommended. This has important implications for how information is spread and consumed online, suggesting a need for more transparency and fairness in how these algorithms work. In summary, this paper highlights the need for a more inclusive approach to how digital platforms recommend content. By better understanding the impact of YouTube’s algorithms, we can work towards creating a digital space that offers a wider range of perspectives and voices, affording fairness, and enriching everyone’s online experience.”

5. 5

   Toxic algorithms wouldn't let them go - now Australia wants an off switch

   BBC NewsPublished Oct 16, 2026Checked Oct 16, 2026

   “Australia has proposed a switch to force social media platforms to let users turn off recommended content.”