Evidence map›Paper›PMID 38698157›Full record

ArticleScientific reports2024

A multilingual analysis of pro Russian misinformation on Twitter during the Russian invasion of Ukraine.

Cameron Lai, Fujio Toriumi, Mitsuo Yoshida

Abstract read
In one paragraph

Article in Scientific reports, 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

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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

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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.

Cameron LaiGraduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, Japan. cameron.lai@torilab.net.
Fujio ToriumiGraduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, Japan.
Mitsuo YoshidaInstitute of Business Sciences, University of Tsukuba, Tokyo, 112-0012, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Russian government has long since engaged in an information campaign of propaganda and disinformation as a major part of foreign policy. This has been taken to new heights since the invasion of Ukraine in February 2022. In this study, we investigate pro-Russian misinformation within the opening weeks of the invasion in 6 languages: English, Japanese, Spanish, French, German, and Korean. Using Twitter data, we apply a combination of network and language embedding models to identify popular topics of misinformation amongst users in each language. Despite English users forming the most dominant language base on Twitter, we find that the popularity of misinformation in Japanese regularly outstrips English for certain topics. Misinformation shared by Spanish users is also over-represented in proportion to its much smaller user base. Our results provide insight into the current state of misinformation in each language. While we discuss some of the possible drivers behind the factors such as language over-representation, our study also highlights the need for further cross-lingual misinformation research in order to better understand this phenomena in a truly global context.

Indexed as

CommunicationLanguageSocial MediaHumansMultilingualismRussiaUkraineMisinformationNetworksSocial mediaText embeddingTwitter

Identifiers

PMID38698157
PMCPMC11065866

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.