Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
This provides a large-scale resource for researchers studying COVID-19 phenomena, but it is incremental as it builds on existing data collection and modeling approaches.
The researchers tackled the problem of studying COVID-19 by creating Mega-COV, a billion-scale, multilingual Twitter dataset covering 268 countries and 100+ languages, and developed models for pandemic-related tweet identification with 97% F1 and misinformation detection with 92% F1.
We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets (~169M tweets). We release tweet IDs from the dataset. We also develop and release two powerful models, one for identifying whether or not a tweet is related to the pandemic (best F1=97%) and another for detecting misinformation about COVID-19 (best F1=92%). A human annotation study reveals the utility of our models on a subset of Mega-COV. Our data and models can be useful for studying a wide host of phenomena related to the pandemic. Mega-COV and our models are publicly available.