We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). We release tweet IDs from the dataset , hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems.\\%DIF > ) for teasing apart messages related to the pandemic from those that are not. Our data and models can be useful for studying various phenomena related to the pandemic and accelerating viable solutions to associated problems. Our data and models are publicly available for research.
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 a powerful model (acc=94\\%DIF > ) for teasing apart messages related to the pandemic from those that are not. Our data and models can be useful for studying various phenomena related to the pandemic and accelerating viable solutions to associated problems. Our data and models are publicly available for research.
R
234
268
R
65
100+
R
~32M
169M
R
, hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems.
. We also develop and release a powerful model (acc=94