Evidence mapPaperPMID 42455842Full record

ArticlePloS one2026

An analytical and experimental study of the energy transition discourse on YouTube.

Aleix Bassolas, Piero Birello, Julian Vicens

Abstract read
In one paragraph

Article in PloS one, 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

3 authors.

Aleix BassolasBig Data and Data Science Unit, Eurecat, Centre Tecnològic de Catalunya, Barcelona, Spain.ORCID https://orcid.org/0000-0001-5588-2117
Piero BirelloBig Data and Data Science Unit, Eurecat, Centre Tecnològic de Catalunya, Barcelona, Spain.ORCID https://orcid.org/0009-0005-9883-9457
Julian VicensBig Data and Data Science Unit, Eurecat, Centre Tecnològic de Catalunya, Barcelona, Spain.ORCID https://orcid.org/0000-0003-0643-0469

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Energy production and management face significant political, economic, and environmental challenges. The rise in information consumption through social media can, under certain conditions, reduce the visibility and uptake of reliable knowledge, particularly in environments characterised by high information overload, algorithmic amplification, and low media literacy. This study examines the ideas discussed in the energy transition content on YouTube, assesses the most effective methods of communicating knowledge and information, and identifies the most engaged audiences. We examine videos related to the subject, analysing the themes discussed, the language used, and the emotions conveyed on YouTube, linking language formality to user engagement. To test the relationship experimentally, original content was uploaded to YouTube through two mirror channels containing the same material but using different levels of language formality. We conducted a systematic statistical analysis of engagement data collected from YouTube and the Google Ads platform. The YouTube engagement suggests that the conversational channel reaches a broader audience, although retention across video segments varies. While user retention was higher in the early segments for the conversational content, a higher retention in the mid to late sections of academic videos was found. Data from the Google Ads platform provided a deeper understanding of engagement across user profiles. Younger individuals and women show greater engagement regardless of language style, although women demonstrate relatively greater interest in the academic content.

Indexed as

Social MediaCommunicationDigital MediaFemaleHumansInternetMedia Exposure

Identifiers

PMID42455842
PMCPMC13372142

What Socratic 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 Socratic graph.