Evidence map›Paper›PMID 40726606›Full record

ArticleEPJ data science2025

Safe spaces or toxic places? Content moderation and social dynamics of online eating disorder communities.

Kristina Lerman, Minh Duc Chu, Charles Bickham, Luca Luceri, Emilio Ferrara

Abstract read
In one paragraph

Article in EPJ data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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

5 authors.

Kristina LermanInformation Sciences Institute, University of Southern California, Marina del Rey, CA USA.
Minh Duc ChuInformation Sciences Institute, University of Southern California, Marina del Rey, CA USA.
Charles BickhamInformation Sciences Institute, University of Southern California, Marina del Rey, CA USA.
Luca LuceriInformation Sciences Institute, University of Southern California, Marina del Rey, CA USA.
Emilio FerraraInformation Sciences Institute, University of Southern California, Marina del Rey, CA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. While these platforms can provide valuable support networks, they may also amplify harmful content that glorifies disordered cognition and self-destructive behaviors. While social media platforms have implemented various content moderation strategies, from stringent to laissez-faire approaches, we lack a comprehensive understanding of how these different moderation practices interact with user engagement in online communities around these sensitive mental health topics. This study addresses this knowledge gap through a comparative analysis of eating disorder discussions across Twitter/X (2.6M tweets), Reddit (178K submissions), and TikTok (14K videos) spanning from 2019-2023. Our findings reveal that while users across all platforms engage similarly in expressing concerns and seeking support, platforms with weaker moderation (like Twitter/X) enable the formation of toxic echo chambers that amplify pro-anorexia rhetoric. These results demonstrate how moderation strategies significantly influence the development and impact of online communities, particularly in contexts involving mental health and self-harm.

Indexed as

Content moderationEating disordersMental healthRedditTikTokTwitter/X

Identifiers

PMID40726606
PMCPMC12296748

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.