Infodemiology uses web-based data to inform public health policymakers. This study aimed to examine the diffusion of Arabic language discussions and analyze the nature of Internet search behaviors related to the global COVID-19 pandemic through two platforms (Twitter and Google Trends) in Saudi Arabia. A set of Twitter Arabic data related to COVID-19 was collected and analyzed. Using Google Trends, internet search behaviors related to the pandemic were explored. Health and risk perceptions and information related to the adoption of COVID-19 infodemic markers were investigated. Moreover, Google mobility data was used to assess the relationship between different community activities and the pandemic transmission rate. The same data was used to investigate how changes in mobility could predict new COVID-19 cases. The results show that the top COVID-19–related terms for misinformation on Twitter were folk remedies from low quality sources. The number of COVID-19 cases in different Saudi provinces has a strong negative correlation with COVID-19 search queries on Google Trends (Pearson r = −0.63) and a statistical significance (p < 0.05). The reduction of mobility is highly correlated with a decreased number of total cases in Saudi Arabia. Finally, the total cases are the most significant predictor of the new COVID-19 cases.

中文翻译：

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COVID-19 的信息流行病学和信息监督研究：推特和谷歌趋势分析

信息流行病学使用基于网络的数据为公共卫生政策制定者提供信息。本研究旨在通过沙特阿拉伯的两个平台（Twitter 和 Google 趋势）检查阿拉伯语讨论的传播情况，并分析与全球 COVID-19 大流行相关的互联网搜索行为的性质。收集和分析了一组与 COVID-19 相关的 Twitter 阿拉伯语数据。使用谷歌趋势，探索了与大流行相关的互联网搜索行为。调查了与采用 COVID-19 信息流行标记相关的健康和风险认知和信息。此外，谷歌移动数据用于评估不同社区活动与大流行传播率之间的关系。相同的数据用于研究流动性的变化如何预测新的 COVID-19 病例。结果表明，Twitter 上与 COVID-19 相关的主要错误信息术语是来自低质量来源的民间疗法。沙特不同省份的 COVID-19 病例数与谷歌趋势上的 COVID-19 搜索查询呈强烈负相关（Pearsonr = -0.63) 和统计显着性 ( p < 0.05)。流动性的减少与沙特阿拉伯总病例数的减少高度相关。最后，总病例数是新 COVID-19 病例的最重要预测指标。