  This paper describes a large global dataset on people's discourse and
responses to the COVID-19 pandemic over the Twitter platform. From 28 January
2020 to 1 June 2022, we collected and processed over 252 million Twitter posts
from more than 29 million unique users using four keywords: "corona", "wuhan",
"nCov" and "covid". Leveraging probabilistic topic modelling and pre-trained
machine learning-based emotion recognition algorithms, we labelled each tweet
with seventeen attributes, including a) ten binary attributes indicating the
tweet's relevance (1) or irrelevance (0) to the top ten detected topics, b)
five quantitative emotion attributes indicating the degree of intensity of the
valence or sentiment (from 0: extremely negative to 1: extremely positive) and
the degree of intensity of fear, anger, sadness and happiness emotions (from 0:
not at all to 1: extremely intense), and c) two categorical attributes
indicating the sentiment (very negative, negative, neutral or mixed, positive,
very positive) and the dominant emotion (fear, anger, sadness, happiness, no
specific emotion) the tweet is mainly expressing. We discuss the technical
validity and report the descriptive statistics of these attributes, their
temporal distribution, and geographic representation. The paper concludes with
a discussion of the dataset's usage in communication, psychology, public
health, economics, and epidemiology.
