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

在大流行期间，公众充分了解该疾病、其传播和必要的应对措施至关重要。在当前的Covid-19危机期间，事实证明，在受众的知识、自我效能和坚持措施的意愿方面，科学家的直接沟通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产生共鸣，以及公众对科学知识的参与在多大程度上导致信息被证实、反驳和侵蚀，这些问题还没有得到充分的解决。拟议项目的目标是通过分析基于证据的社交媒体活动的比例，以及情感化信息的传播（主要关注恐惧和愤怒），为这个问题做出贡献。基于流行病容易传播虚假信息的发现，我们进一步仔细研究了真正的专家和假专家在沟通方面的差异，以及外行人如何区分他们。我们将对社交媒体内容（传播科学）进行深入的人工编码，通过计算方法（计算机科学）将其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).