ABSTRACT

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.

摘要

随着新型冠状病毒在世界范围内的传播，工作、娱乐、娱乐、社交和会议都转移到了网上。社交媒体上的讨论激增，考虑到不确定性和新政策，COVID-19 仍然是包括 Twitter 在内的所有此类平台上的热门话题。本研究探讨了影响 Twitter 用户分享 COVID-19 内容的因素。该分析是使用 57,000 多条提到 COVID-19 和相关关键词的推文进行的。这些推文采用了自然语言处理 (NLP) 技术，如主题建模、命名实体关系、情感和情感分析以及语言特征提取。这些方法生成的特征可以帮助解释推文的转发次数。结果表明，带有命名实体（人、组织、和地点）、负面情绪的表达（愤怒、厌恶、恐惧和悲伤）、提及心理健康、乐观的内容和更长的推文被分享（转推）的机会更高。另一方面，带有更多主题标签和用户提及的推文不太可能被分享。