Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers

• Published in: PloS one Vol. 17; no. 3; p. e0264794
• Main Authors: Yu, Haiyan, Yang, Ching-Chi, Yu, Ping, Liu, Ke
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.03.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial intelligence; Attitude; Biology and Life Sciences; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - virology; Diffusion effects; Digital media; Emotions; Global health; Humans; Influence; Machine learning; Medicine and Health Sciences; Organizations; Pandemics; Polarity; Public health; SARS-CoV-2 - isolation & purification; Sentiment analysis; Social Media; Social networks; Social Sciences; Viral diseases; United States; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264794

Coronavirus disease 2019 (COVID-19) has triggered an enormous number of discussion topics on social media Twitter. It has an impact on the global health system and citizen responses to the pandemic. Multiple responses (replies, favorites, and retweets) reflect the followers' attitudes and emotions towards these tweets. Twitter data such as these have inspired substantial research interest in sentiment and social trend analyses. To date, studies on Twitter data have focused on the associational relationships between variables in a population. There is a need for further discovery of causality, such as the influence of sentiment polarity of tweet response on further discussion topics. These topics often reflect the human perception of COVID-19. This study addresses this exact topic. It aims to develop a new method to unveil the causal relationships between the sentiment polarity and responses in social media data. We employed sentiment polarity, i.e., positive or negative sentiment, as the treatment variable in this quasi-experimental study. The data is the tweets posted by nine authoritative public organizations in four countries and the World Health Organization from December 1, 2019, to May 10, 2020. Employing the inverse probability weighting model, we identified the treatment effect of sentiment polarity on the multiple responses of tweets. The topics with negative sentiment polarity on COVID-19 attracted significantly more replies (69±49) and favorites (688±677) than the positive tweets. However, no significant difference in the number of retweets was found between the negative and positive tweets. This study contributes a new method for social media analysis. It generates new insight into the influence of sentiment polarity of tweets about COVID-19 on tweet responses.