Evidence map›Paper›PMID 42816553›Full record

ArticleNature human behaviour2026

Introducing AI to an online petition platform changed outputs but not outcomes.

Isabel Corpus, Eric Gilbert, Allison Koenecke, Mor Naaman

Abstract read
PubMed Publisher
In one paragraph

Article in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Isabel CorpusDepartment of Information Science, Cornell University, Ithaca, NY, USA. isc36@cornell.edu.ORCID http://orcid.org/0009-0007-5794-4407
Eric GilbertSchool of Information, University of Michigan, Ann Arbor, MI, USA.
Allison KoeneckeDepartment of Information Science, Cornell University, Ithaca, NY, USA.
Mor NaamanDepartment of Information Science, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0002-6436-3877

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid integration of artificial intelligence (AI) writing tools into online platforms raises critical questions about their impact on content production and outcomes. We leverage a unique natural experiment on Change.org, a leading social advocacy platform, to causally investigate the effects of an in-platform 'write with AI' tool. To understand the impact of the AI integration, we collected 1.5 million petitions and used a difference-in-differences analysis. Here we show that, at a platform level, in-platform AI access substantially altered the lexical features of petitions and increased petition homogeneity but did not improve petition outcomes. We confirmed that the results were consistent in a separate analysis of repeat petition writers who wrote petitions before and after introduction of the AI tool. The results suggest that while AI writing tools can profoundly reshape online content, their practical utility for improving desired outcomes may be less beneficial than anticipated and introduce unintended consequences such as content homogenization.

Identifiers

What OpenQuestion holds

Textmetadata
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.