DOI: 10.3886/e120321
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.