Science communication during pandemics: The role of public engagement in social media discussions

在大流行期间，至关重要的是让公众充分了解这种疾病、其传播和必要的应对措施。在当前的新冠肺炎危机中，在受众的知识、自我效能感和遵守措施的意愿方面，科学家的直接沟通被证明比一般媒体消费更有效(Szczuka，Meinert，&Krämer，2020，预印本)。然而，这种直接的科学传播如何在社交媒体上引起共鸣，以及公众对科学知识的参与在多大程度上导致这些信息被证实或矛盾和侵蚀，这一问题还没有得到充分的解决。拟议项目的目标是通过分析以证据为基础的社交媒体活动的比例以及传播带有情感色彩的信息(主要侧重于恐惧和愤怒)，为这一问题作出贡献。基于流行病容易传播虚假信息的发现，我们进一步仔细审查了实际专家和假装专家在沟通方面的差异，以及外行人能在多大程度上区分他们。我们将对社交媒体内容进行深入的人工编码(传播学)，通过计算方法(计算机科学)将这种编码应用于已经收集的大型数据集，并通过实验方法(心理学)分析心理影响和机制。

During times of pandemics, it is vital that the public is well informed about the disease, its dissemination and necessary counter-measures. During the current Covid-19 crisis, direct communication by scientists proved to be more effective than general media consumption regarding the audience’s knowledge, self-efficacy and intention to adhere to the measures (Szczuka, Meinert, & Krämer, 2020, preprint). However, it has not been sufficiently addressed how this direct science communication resonates in social media and to what extend the public engagement with scientific knowledge leads to the messages being corroborated or contradicted and eroded. The goal of the proposed project is to contribute to this question by analyzing the proportion of evidence-based social media activities as well as the dissemination of emotionally toned messages (primarily focusing on fear and anger). Based on findings that pandemics are prone to the dissemination of disinformation, we further scrutinize how actual and feigned experts differ in their communication and how well laypeople can distinguish them. We will employ in-depth manual coding of social media content (communication science), apply this to – already collected - large data sets by computational methods (computer science) and analyse psychological effects and mechanisms by experimental methods (psychology).