ArticleFrontiers in artificial intelligence2026
Analyzing gendered patterns in sentiments in comments under STEM YouTube channels.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores gender-based disparities in sentiments and discourse in comments on YouTube channels focused on science, technology, engineering, and mathematics (STEM). Although there have been considerable efforts toward achieving gender equality, biases and inequalities still persist in the representation of women in STEM, including in online communication spaces. A mixed-methods approach was used, based on a dataset of comments collected from over 100 curated STEM YouTube channels using the YouTube Data API. The comments were cleaned and preprocessed, and information regarding the gender of content creators was identified. The RoBERTa model was used for sentiment annotation, while Latent Dirichlet Allocation (LDA) was applied for topic modelling. The results show a clear difference in sentiment distribution between genders. Negative comments were more frequent in female-hosted channels and were often related to personal criticism, objectification, and appearance-based remarks, whereas comments on male-hosted channels were more neutral and focused on content. The findings indicate the presence of gender bias in online discussions related to STEM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.