CovidTracker: A comprehensive Covid-related social media dataset for NLP tasks

TL;DR

This paper introduces CovidTracker, a comprehensive social media dataset from the UK related to Covid-19, enabling research on public sentiment, misinformation, and social responses during health crises.

Contribution

It provides a new benchmark database of Covid-19 social media posts with initial thematic analysis for NLP research and public health insights.

Findings

Taxonomy of key themes in social media posts

Initial analysis of public sentiment and misinformation

Resource supports future public health policy research

Abstract

The Covid-19 pandemic presented an unprecedented global public health emergency, and concomitantly an unparalleled opportunity to investigate public responses to adverse social conditions. The widespread ability to post messages to social media platforms provided an invaluable outlet for such an outpouring of public sentiment, including not only expressions of social solidarity, but also the spread of misinformation and misconceptions around the effect and potential risks of the pandemic. This archive of message content therefore represents a key resource in understanding public responses to health crises, analysis of which could help to inform public policy interventions to better respond to similar events in future. We present a benchmark database of public social media postings from the United Kingdom related to the Covid-19 pandemic for academic research purposes, along with some initial analysis, including a taxonomy of key themes organised by keyword. This release supports the findings of a research study funded by the Scottish Government Chief Scientists' Office that aims to investigate social sentiment in order to understand the response to public health measures implemented during the pandemic.