Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361611 | PMC |
| http://dx.doi.org/10.1016/j.dib.2025.111960 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ResDeepGS: A deep learning-based method for crop phenotype prediction.
Methods
September 2025
School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China. Electronic address:
Genomic selection (GS) is a breeding technique that utilizes genomic markers to predict the genetic potential of crops and animals. This approach holds significant promise for accelerating the improvement of agronomic traits and addressing food security challenges. While traditional breeding methods based on statistical or machine learning techniques have been useful in predicting traits for some crops, they often fail to capture the complex interactions between genotypes and phenotypes.
View Article and Find Full Text PDFUnveiling the Pathogenicity of Allosteric Protein Mutations via Multifaceted Feature Ensembling.
Methods
September 2025
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Electronic address:
Allosteric proteins play a central role in biological processes and systems. Identifying the biological impact of mutations on allosteric proteins and the phenotypes they influence during disease initiation and progression presents a significant challenge. In theory, computational methods have the potential to facilitate the interpretation of genetic variants in allosteric proteins on a large scale.
View Article and Find Full Text PDFEndoChat: Grounded multimodal large language model for endoscopic surgery.
Med Image Anal
August 2025
The Chinese University of Hong Kong, 999077, Hong Kong Special Administrative Region of China. Electronic address:
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted surgery, MLLMs can serve as effective tools for surgical training and guidance. However, there is still a deficiency of MLLMs specialized for surgical scene understanding in endoscopic procedures.
View Article and Find Full Text PDFBenchmarking AI-driven acoustic monitoring for floating marine debris: Challenges in deep learning-based debris extraction.
Mar Pollut Bull
September 2025
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8563, Japan. Electronic address:
Existing studies have identified a substantial amount of invisible floating debris in low-visibility marine environments, in addition to debris on the surface and seabed. These suspended pollutants represent a persistent and dynamic threat to marine ecosystems and maritime safety. Although sonar technology facilitates debris monitoring in low-visibility waters, the automatic extraction of small and weakly contrasted debris targets remains a critical challenge.
View Article and Find Full Text PDFEnhancing fake news detection with transformer-based deep learning: A multidisciplinary approach.
PLoS One
September 2025
Department of Computer Science, COMSATS University Islamabad, Sahiwal, Pakistan.
The widespread dissemination of fake news presents a critical challenge to the integrity of digital information and erodes public trust. This urgent problem necessitates the development of sophisticated and reliable automated detection mechanisms. This study addresses this gap by proposing a robust fake news detection framework centred on a transformer-based architecture.
View Article and Find Full Text PDF