Five-year Twitter corpus (14,201 tweets) with SetFit sentiment classifier and BERTopic modelling pipeline¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A researcher-created dataset and analysis pipeline comprising 14,201 English-language U.S. tweets about AI, education, and ethics, classified with SetFit and modelled with BERTopic by Bagale et al.
Components¶
Claims¶
- Topic modelling yielded 16 topics dominated by four: AI in Education & Pedagogy (39.43%), Applied ML & Data Science (21.33%), AI Ethics Risk & Governance (20.83%), and ChatGPT for Teaching, Writing & Coding (7.13%) [+W]
- Public sentiment toward AI in education on Twitter was predominantly positive (81.65% of tweets) across 2019–2024 [+W]
Related Products and Programmes¶
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Key Sources¶
- Bagale, A.; Mehjabin, N.; Ünlü, A.; Johri, A. (2025). A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data. doi:10.48550/arxiv.2607.12295
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
five-year-twitter-corpus-14201-tweets-with-setfit-sentiment-classifier-and-bertopic-modell |
title |
Five-year Twitter corpus (14,201 tweets) with SetFit sentiment classifier and BERTopic modelling pipeline |
description |
A researcher-created dataset and analysis pipeline comprising 14,201 English-language U.S. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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