Evidence map›Paper›PMID 40763170›Full record

ArticlePloS one2025

Linguistic mechanisms of knowledge-exchange in a dark-web money laundering forum.

Emily Chiang

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. 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

1 author.

Emily ChiangAston Institute for Forensic Linguistics, Aston University, Birmingham, United Kingdom.ORCID https://orcid.org/0000-0002-0216-1719

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Money laundering facilitates serious crime, enables the expansion of criminal operations, and destabilises economies. Extant scholarship is largely concerned with anti-money laundering approaches, with far less attention being paid to the language and behaviours of the individuals who engage in money laundering. 'Dark-web' discussion fora are prime loci for illicit knowledge exchange and key enablers of money laundering, yet, are underexplored as sites for understanding the online activities and behaviours of users. This paper reports on a corpus-assisted discourse analysis of one such forum, guided by research questions around the key topics and common linguistic strategies by which knowledge is exchanged within a large community of individuals interested in money laundering, and the ways in which this community serves its members. The analysis identifies the forum as an extremely efficient and productive site for knowledge-exchange and thus 'criminal upskilling', which is attributed to three core characteristics: a strict adherence to community rules, a highly knowledgeable user base, and a culture of friendliness and reciprocity.

Identifiers

PMID40763170
PMCPMC12324082

What OpenQuestion holds

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LicenceCC BY
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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.