DOI: 10.3886/e120321v12-178871
title: COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes

description: 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.
description: Response Rates: Existing observational data from Twitter users' public posts (tweets)
description: Presence of Common Scales: 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.
description: 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”.
description: Twitter posts in English language that are labelled with topics, sentiments and emotions attributes using pre-trained algorithms. Smallest Geographic Unit: Geographic is labelled to the country/region level
description: other; web scraping; The
latest dataset version (V12, June 2022) has the following main updates: a) Full
data coverage extended to cover 28 January 2020 – 1 June 2022 (2 years and 4
months), b) Country-specific CSV files download covers 30 representative
countries, c) Added new vaccine-related data covering from 3 November 2021 to 1
June 2022 (8 months), d) an updated discussion on the dataset’s usage.