Evidence map›Paper›PMID 41280165›Full record

ArticleFrontiers in psychology2025

Normalizing toxicity: the role of recommender algorithms for young people's mental health and social wellbeing.

Kaitlyn Regehr, Caitlin Shaughnessy, Minzhu Zhao, Idil Cambazoglu, Alfie Turner, Nicola Shaughnessy

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Online Searches for Gun-Related Harm.JAMA network open · 2026
    Article
  3. Eating disorders and the incel community.Frontiers in psychology · 2026
    Article
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kaitlyn RegehrUniversity College London, London, United Kingdom.
Caitlin ShaughnessyUniversity College London, London, United Kingdom.
Minzhu ZhaoUniversity College London, London, United Kingdom.
Idil CambazogluUniversity College London, London, United Kingdom.
Alfie TurnerUniversity College London, London, United Kingdom.
Nicola ShaughnessyUniversity College London, University of Kent, Canterbury, Kent, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article explores how social media recommendation systems shape the digital consumption practices of young people and the potential implications for mental health and wellbeing. It examines how the consumption of increasingly radical content, with a focus on gender-based violence and misogyny, is presented on young people's feeds in increasingly high dosages, which has significant implications for young people's social development. Employing a mixed-methods approach, this research draws on three data sources: (i) long-form interviews with young people, (ii) algorithmic analysis of over 1,000 social media videos, and (iii) roundtable discussions and interviews with school leaders from across England and Wales. These methods were used to triangulate how digital environments encourage and normalize harmful ideologies, normalizing radical content, and the affective impacts of this content on young people's wellbeing. The study presents three main findings. First, recommendation systems amplify and subsequently normalize harmful ideologies, increasing users' exposure to radical material. Second, misogynistic content is often presented as entertainment, which enables it to gain high levels of traction on social media platforms. As a result, hateful ideologies and misogynistic tropes appear in young people's behaviors, which may have significant impacts on their mental health and peer relationships. Our findings suggest the need for a significant change in approaches to digital literacy, education and policy to support young people's wellbeing and social development in digital spaces.

Indexed as

algorithm analysisdigital literacymental healthmisogynyneurodiversitysocial media

Identifiers

PMID41280165
PMCPMC12630993

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.