Misinformation entails the dissemination of falsehoods that leads to the slow fracturing of society via decreased trust in democratic processes, institutions, and science. The public has grown aware of the role of social media as a superspreader of untrustworthy information, where even pandemics have not been immune. In this paper, we focus on COVID-19 misinformation and examine a subset of 2.1M tweets to understand misinformation as a function of engagement, tweet content (COVID-19- vs. non-COVID-19-related), and veracity (misleading or factual). Using correlation analysis, we show the most relevant feature subsets among over 126 features that most heavily correlate with misinformation or facts. We found that (i) factual tweets, regardless of whether COVID-related, were more engaging than misinformation tweets; and (ii) features that most heavily correlated with engagement varied depending on the veracity and content of the tweet.
翻译:错误信息导致传播错误信息,导致社会通过减少对民主进程、机构和科学的信任而缓慢分裂。公众日益认识到社交媒体作为超大传播不可信信息(即使大流行病也未能幸免)的作用。在本文中,我们侧重于COVID-19错误信息,并审查2.1M Twitter的一组内容,以理解错误信息作为参与、推特内容(COVID-19诉非COVID-19)和真实性(误差或事实性)的函数。通过相关分析,我们展示了126多个与错误信息或事实关系最密切的特征中最相关的子集。我们发现:(一) 事实性推特,无论是否与COVID相关,都比错误推文更具吸引力;(二) 与参与关系最密切的特征,取决于推文的真实性和内容。