Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic

Abstract

Social media (e.g., Twitter) has been an extremely popular tool for public health surveillance. The novel coronavirus disease 2019 (COVID-19) is the first pandemic experienced by a world connected through the internet. We analyzed 105+ million tweets collected between March 1 and May 15, 2020, and Weibo messages compiled between January 20 and May 15, 2020, covering six languages (English, Spanish, Arabic, French, Italian, and Chinese) and represented an estimated 2.4 billion citizens worldwide. To examine fine-grained emotions during a pandemic, we built machine learning classification models based on deep learning language models to identify emotions in social media conversations about COVID-19, including positive expressions (optimistic, thankful, and empathetic), negative expressions (pessimistic, anxious, sad, annoyed, and denial), and a complicated expression, joking, which has not been explored before. Our analysis indicates a rapid increase and a slow decline in the volume of social media conversations regarding the pandemic in all six languages. The upsurge was triggered by a combination of economic collapse and confinement measures across the regions to which all the six languages belonged except for Chinese, where only the latter drove conversations. Tweets in all analyzed languages conveyed remarkably similar emotional states as the epidemic was elevated to pandemic status, including feelings dominated by a mixture of joking with anxious/pessimistic/annoyed as the volume of conversation surged and shifted to a general increase in positive states (optimistic, thankful, and empathetic), the strongest being expressed in Arabic tweets, as the pandemic came under control.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media

“…Specifically, this method can identify and track topics related to public opinion on Twitter as well as mine the public’s emotional characteristics in different fields. Zhang et al [ 18 ] studied algorithms to monitor a public opinion network, compared and analyzed the advantages and disadvantages of different text classification and emotional tendency algorithms, and discussed future trends in algorithm development to monitor public panic during the COVID-19 pandemic in different geographical locations. Microblogs contain emotional information from social users.…”

Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders

“…While the lowest value of valence is found at Phase 3 (green), between August 4th and 6th 2021, corresponding with the announcement of the need for booster doses and the debate on compulsory vaccination. At an international scale, we can also detect a similar evolution of the global conversation and shifting social moods during the COVID-19 pandemic [ 59 ].…”

PyArabic: A Python package for Arabic text

“…• Text preprocessing (remove diacritics, tokenization, etc. ): Zhang et al (2021) • Lexical resources (Choe et al, 2020) • Text similarity (Mouty & Gazdar, 2019) PyArabic was inspired by Ar-PHP(Al-Shamaa, 2022), an Arabic library for the PHP programming language that provides basic routines for web developers. Then the two libraries grow together through collaborations, and they are inspired mutually by each other.…”