Evidence map›Paper›PMID 42458152›Full record

ArticleBehavior research methods2026

Collecting data from YouTube using the official API: A tutorial and a ready-to-use tool.

Kévin Campos-Moinier

Abstract read
PubMed Publisher
In one paragraph

Article in Behavior research methods, 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

1 author.

Kévin Campos-MoinierUniversity of Strasbourg, LPC UR 4440, 12 Rue Goethe, F-67000, Strasbourg, France. kevin.campos@unistra.fr.ORCID http://orcid.org/0000-0001-7453-4839

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online data from social media platforms provide an observational window into a wide range of psychological phenomena and real-world social interactions. Accessing these data typically requires the use of application programming interfaces (APIs), which are designed to ensure compliance with the legal and ethical frameworks governing the access, processing, and storage of potentially identifiable information. However, implementing API-based data collection within a dedicated program remains a major barrier for many researchers. This article addresses this challenge by introducing a ready-to-use data collection tool based on the official YouTube Data API (v3) and providing step-by-step guidance for its use. Geared toward researchers and practitioners with little or no prior programming experience, the tutorial enables users to implement transparent and reproducible API-based data collection procedures in their research. The tool and a fully documented interactive Python notebook are freely available via a public GitHub repository.

Indexed as

Data CollectionSocial MediaSoftwareHumansInternetUser-Computer InterfaceAPI-based data collectionOpen scienceSocial media dataYouTube Data API

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.