Jiang Julie, Ren Xiang, Ferrara Emilio
Information Sciences Institute University of Southern California Marina Del Rey, CA United States.
Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles, CA United States.
JMIRx Med. 2021 Aug 5;2(3):e29570. doi: 10.2196/29570. eCollection 2021 Jul-Sep.
Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one's confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication.
We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States.
First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users.
We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest.
We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.
2020年社交媒体上的讨论很大程度上被新冠疫情主导。现有研究表明,新冠疫情相关的讨论高度政治化,政治倾向与对病毒的相信和怀疑相关。就像那些政治化的话题一样,人们可能会陷入信息茧房,即只接触到他们已经认同的信息,从而强化了自身的确认偏差。理解信息传播与政治倾向之间的关系对于有效的公共卫生传播至关重要。
我们旨在研究美国推特上与新冠疫情相关讨论的两极分化程度,并考察信息茧房的结构。
首先,我们展示了转发-双向编码器表征变换器(Retweet-BERT),这是一种通过利用语言特征和网络结构来估计用户倾向的可扩展且高度准确的模型。然后,通过分析转发-双向编码器表征变换器预测的用户倾向,我们对党派用户的特征有了新的见解。
我们观察到,右倾用户在新冠疫情信息的生产和消费方面明显更加活跃和积极发声。我们还发现,大多数有高度影响力的用户都是党派性的,这可能会导致进一步的两极分化。重要的是,虽然在右倾和左倾群体中都存在信息茧房,但右倾群体在其信息茧房内的联系要紧密得多,并且与其他群体相互隔离。
我们提供了实证证据,表明政治信息茧房很普遍,尤其是在右倾群体中,这会加剧符合现有用户观点的信息的接触。我们的研究结果对于开展有效的公共卫生宣传活动以及促进事实性信息在网上的传播具有更广泛的意义。