A large-scale dataset of AI-related tweets: Structure and descriptive statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a curated and anonymized dataset of tweets related to artificial intelligence (AI), comprising 893,076 entries collected using the Twitter API between January 1, 2017, and July 19, 2021. These tweets were extracted from a larger initial corpus using the keyword "Artificial Intelligence" and subsequently filtered to ensure data quality, multilingual coverage, and public accessibility. The final dataset includes structured metadata such as media elements (images, videos, and URLs), user engagement metrics (likes, retweets, replies), hashtags, language codes, and temporal indicators (hour and weekday of posting). While additional linguistic features-such as text length and tokenization-were used in internal analyses, they are not included in the public release. This dataset offers a robust foundation for research on the evolution of public discourse surrounding AI, including sentiment analysis, topic modeling, social engagement dynamics, and policy-relevant evaluations. It is openly available through established repositories and adheres to the FAIR principles, facilitating transparency, reproducibility, and interdisciplinary applications in computational social science, natural language processing, and AI governance research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it