A Biomedically oriented automatically annotated Twitter COVID-19 Dataset

TL;DR

This paper introduces a large, automatically annotated Twitter dataset related to COVID-19, enabling biomedical research by providing high-relevance clinical data at scale, addressing the scarcity of large, annotated social media datasets.

Contribution

The authors created and publicly released a 120 million tweet dataset with biomedical annotations, using best practices and SpaCy-based frameworks, for COVID-19 research.

Findings

Evaluation of annotation frameworks against a gold-standard dataset.

Selection of the best SpaCy-based method for automatic annotation.

Release of a large, annotated COVID-19 Twitter dataset for biomedical research.

Abstract

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontraditional sources of clinical data to characterize the disease in near real-time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-COVID). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations do not generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.