ArticleData in brief2025
A large-scale dataset of AI-related tweets: Structure and descriptive statistics.
Article in Data in brief, 2025. 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
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed 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.
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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.