Oliveira Francisco Bráulio, Mougouei Davoud, Haque Amanul, Sichman Jaime Simão, Dam Hoa Khanh, Evans Simon, Ghose Aditya, Singh Munindar P
University of Sao Paulo, Sao Paulo, Brazil.
Deakin University, Burwood, VIC, Australia.
Online Soc Netw Media. 2023 Jun 14:100253. doi: 10.1016/j.osnem.2023.100253.
The media has been used to disseminate public information amid the Covid-19 pandemic. However, the Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to the Covid-19 news, we study user comments on the news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employ a deep-learning-based model to identify one of the 6 Ekman's basic emotions, or the absence of emotional expression, in comments to the Covid-19 news, and an implementation of Latent Dirichlet Allocation (LDA) to identify 12 different topics in the news messages. Our analysis finds that while nearly half of the user comments show no significant emotions, negative emotions are more common. Anger is the most common emotion, particularly in the media and comments about political responses and governmental actions in the United States. Joy, on the other hand, is mainly linked to media outlets from the Philippines and news on vaccination. Over time, anger is consistently the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing and occasionally spiking with news of Covid-19 variants, cases, and deaths. Emotions also vary across media outlets, with Fox News having the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness is highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Also, fear is most evident in the comments to the news from The Times of India.
在新冠疫情期间,媒体被用于传播公共信息。然而,新冠疫情相关新闻引发了人们的情绪反应,影响了他们的心理健康,并导致新闻回避行为。为了解对新冠疫情相关新闻的情绪反应,我们研究了2020年1月至2022年12月期间11个国家37家媒体在推特上发布的新闻的用户评论。我们采用基于深度学习的模型来识别对新冠疫情相关新闻评论中六种艾克曼基本情绪之一,或无情绪表达的情况,并运用潜在狄利克雷分配(LDA)方法来识别新闻信息中的12个不同主题。我们的分析发现,虽然近一半的用户评论没有明显情绪,但负面情绪更为常见。愤怒是最常见的情绪,尤其是在美国关于媒体以及政治回应和政府行动的评论中。另一方面,喜悦主要与菲律宾的媒体机构以及疫苗接种新闻相关。随着时间推移,愤怒一直是最普遍的情绪,恐惧在疫情开始时最为普遍,但随着新冠病毒变种、病例和死亡新闻的出现而减少,偶尔会激增。情绪在不同媒体机构之间也存在差异,福克斯新闻的厌恶情绪水平最高,愤怒情绪水平次之,恐惧情绪水平最低。悲伤情绪在公民电视台、南非广播公司和非洲国家电视台这三家非洲媒体机构中最为明显。此外,对《印度时报》新闻的评论中恐惧情绪最为明显。