Storey Veda C, O'Leary Daniel E
Dept. of Computer Information Systems, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 USA.
Marshall School of Business, University of Southern California, Los Angeles, CA USA.
Cognit Comput. 2022 Jul 28:1-24. doi: 10.1007/s12559-022-10025-3.
Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users' opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users' communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users' Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.
科学家和普通民众都在寻找应对新冠疫情广泛影响的方法。当科学家们在实验室忙碌时，其他民众常常转向网络资源来报告他们的经历和担忧，并寻求和分享有关该病毒的知识。在线社交媒体平台上用户生成的文本能够提供有关用户观点和态度演变的宝贵见解。本研究的目的是以四种主要方式分析此类用户披露的文本，以研究疫情期间的人际交流。首先，我们分析整个疫情期间产生的推特推文信息，以了解用户关于新冠病毒的交流情况以及这些交流在疫情期间是如何演变的。其次，我们分析不同推特环境下的语言情感概念（分析性、真实性、影响力和语气概念）（有图片或无图片的推文以及推文与转发推文中的情感）。第三，我们调查推特推文与其他互联网活动形式之间的关系，即谷歌搜索和维基百科页面浏览量。最后，我们创建并使用一个与新冠病毒相关的特定概念词典（例如，味觉丧失症状）来评估推文中这些概念的使用与信息传播以及推特用户产生的影响之间的关系。分析表明，在疫情初期，人们在寻求信息时，情感表达出人意料地少。随着时间的推移，情感表达增多，包括愤怒。此外，有图片和/或视频的推文与无图片和/或视频的推文在文本情感特征上存在统计学上的显著差异。同样，推文与转发推文的情感也存在差异。我们还发现谷歌和维基百科搜索可以预测推文中的情感。最后，一个代表新冠相关概念词典的变量在与用户的推特影响力得分和转发次数相关时具有统计学意义，这说明了新冠病毒对推特和人际交流的总体影响。总体而言，研究结果为人际交流以及人类互联网和社交媒体使用模式提供了见解。这些发现可能有助于应对疫情之外或不同于疫情的全球挑战。
