CovidTracker: A comprehensive Covid-related social media dataset for NLP tasks
Richard Plant, Amir Hussain

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Sentiment Analysis and Opinion Mining
