Evidence map›Paper›PMID 40748996›Full record

ArticlePloS one2025

The Multiple Platforms Effect (MPE): A quantification of how exposure to similarly biased content on multiple online platforms might impact users.

Robert Epstein, Amanda Newland, Thomas Peeler, Basil Thaddeus

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Robert EpsteinAmerican Institute for Behavioral Research and Technology, Vista, California, United States of America.ORCID https://orcid.org/0000-0002-7484-6282
Amanda NewlandAmerican Institute for Behavioral Research and Technology, Vista, California, United States of America.
Thomas PeelerAmerican Institute for Behavioral Research and Technology, Vista, California, United States of America.
Basil ThaddeusAmerican Institute for Behavioral Research and Technology, Vista, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, controlled studies have identified nearly a dozen new forms of manipulation that can be used by search engines, social media platforms, microblogging platforms, and intelligent personal assistants. A recent study has shown that when users were exposed repeatedly to similarly biased content on the same platform, the net impact of those exposures was additive. We now ask the following: What happens when users are exposed to similarly biased content generated by different means on multiple online platforms? In the present experiment, which was randomized, controlled, counterbalanced, and double-blind, we exposed people to similarly biased content generated by different means on three different platforms - simulations of Google Search, Alexa, and X (f.k.a., Twitter) - presented successively and in a random order. We found that the impact of successive exposures was additive for both opinions and voting preferences pertaining to political candidates. Overall, the number of undecided voters voting for the favored candidate increased with each successive platform exposure by 42.4%, then 56.5%, then 66.7% over the pre-exposure level. We speculate that if Big Tech companies share values or political preferences, their net effect on our elections might be considerably greater than the effect of any individual company.

Indexed as

InternetSocial MediaAdultFemaleHumansMalePoliticsSearch Engine

Identifiers

PMID40748996
PMCPMC12316238

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.