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Related Experiment Video

Updated: Oct 26, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

A Biomedically oriented automatically annotated Twitter COVID-19 Dataset.

Luis Alberto Robles Hernandez¹, Tiffany J Callahan², Juan M Banda¹

¹Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303 USA.

|August 3, 2021

Summary

This summary is machine-generated.

Researchers released 120 million automatically annotated tweets for biomedical research. This large dataset aids in analyzing public health trends and Long-COVID using social media data.

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Area of Science:

Biomedical Informatics
Public Health Informatics
Computational Social Science

Background:

Social media data, particularly Twitter, is increasingly used in biomedical research.
The COVID-19 pandemic accelerated the need for real-time, nontraditional clinical data sources.
Manually curated social media datasets are scarce, costly, and often lack generalizability.

Approach:

Leveraged the 2021 Biomedical Linked Annotation Hackathon to develop an automatically annotated tweet dataset.
Implemented best practices to identify tweets with high clinical relevance for biomedical research.
Evaluated multiple SpaCy-based annotation frameworks against a gold-standard dataset to select the optimal annotation method.

Key Points:

A publicly released dataset of over 120 million automatically annotated tweets is now available.
The dataset focuses on tweets with potential clinical relevance for biomedical research applications.
The annotation process utilized and validated advanced natural language processing techniques.

Conclusions:

The release of this large-scale, automatically annotated dataset addresses the limitations of manual curation.
This resource facilitates downstream biomedical research, including real-time disease characterization and analysis of intervention impacts.
The dataset supports broader investigations into public health phenomena and the long-term effects of diseases like COVID-19 (Long-COVID).