悲伤
情绪分析
社会化媒体
厌恶
愤怒
计算机科学
愉快
情感(语言学)
2019年冠状病毒病(COVID-19)
万维网
心理学
情报检索
自然语言处理
社会心理学
传染病(医学专业)
神经科学
病理
疾病
医学
沟通
作者
Krishnadas Nanath,Geethu Joy
标识
DOI:10.1080/0144929x.2021.1941259
摘要
As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared.
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