Abstract

We proposed a conceptual model combining three theories: uses and gratification theory, social networking sites (SNS) dependency theory and social impact theory to understand the factors that predict fake news sharing related to COVID-19. We also tested the moderating role of fake news knowledge in reducing the tendency to share fake news. Data were drawn from social media users (n = 650) in Nigeria, and partial least squares was used to analyse the data. Our results suggest that tie strength was the strongest predictor of fake news sharing related to COVID-19 pandemic. We also found perceived herd, SNS dependency, information-seeking and parasocial interaction to be significant predictors of fake news sharing. The effect of status-seeking on fake news sharing, however, was not significant. Our results also established that fake news knowledge significantly moderated the effect of perceived herd, SNS dependency, information-seeking, parasocial interaction on fake news sharing related to COVID-19. However, tie strength and status-seeking effects were not moderated.

中文翻译：

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模拟影响COVID-19上在线假新闻共享的前因：假新闻知识的调节作用

摘要

我们提出了一个概念模型，该模型结合了三种理论：使用和满足理论，社交网站（SNS）依赖理论和社会影响理论，以了解预测与COVID-19相关的假新闻共享的因素。我们还测试了假新闻知识在减少共享假新闻趋势方面的调节作用。数据来自社交媒体用户（n  = 650），并使用偏最小二乘分析数据。我们的结果表明，联系强度是与COVID-19大流行相关的虚假新闻共享的最强预测因子。我们还发现感知群，SNS依赖性，信息寻求和超社会互动是假新闻共享的重要预测因素。但是，寻求身份对虚假新闻共享的影响并不明显。我们的结果还证实，假新闻知识显着缓解了与COVID-19相关的假新闻共享中的感知群，SNS依赖性，信息寻求，超社会互动的影响。但是，领带强度和寻求地位的影响并未得到缓解。