Misinformation and harmful language are interconnected, rather than distinct, challenges.

Abstract read

In one paragraph

Article in PNAS nexus, 2024. 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

3 authors.

Mohsen MoslehDepartment of Management, University of Exeter Business School, Exeter, EX4 4PU, UK.ORCID https://orcid.org/0000-0001-7313-5035

Rocky ColeSloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.

David G RandSloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.ORCID https://orcid.org/0000-0001-8975-2783

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is considerable concern about users posting misinformation and harmful language on social media. Substantial-yet largely distinct-bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from

Identifiers

PMID38516274

DOI10.1093/pnasnexus/pgae111

PMCPMC10957235

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.