Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context.

中文翻译：

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使用基于 BERT 的信息监控对 COVID 相关讨论进行公众意识和情绪分析

了解 COVID-19 大流行等大型突发卫生事件期间公众关注和情绪的不同方面，对于公共卫生机构制定有效的沟通策略、提供最新和准确的健康信息以及减轻新兴疫情的潜在影响至关重要误传。当前的信息监控系统通常将讨论强度（即相关帖子的数量）作为公众意识的近似值，而在很大程度上忽略了文本中丰富多样的信息以及不同公众关注和情绪的详细信息。在这项研究中，我们通过开发一种基于来自转换器 (BERT) 的双向编码器表示的新型自然语言处理 (NLP) 信息监控工作流程来应对这一重大挑战。我们首先使用较小的 COVID-19 推文样本来开发使用 COVID-Twitter-BERT 的内容分类和情感分析模型。五个确定主题的分类准确度在 0.77 和 0.88 之间。在具有三类分类任务（正面/负面/中性）的情感分析中，BERT 取得了不错的准确率，0.7。然后，我们将内容主题和情感分类器应用于一个更大的数据集，该数据集在 15 个月内包含超过 400 万条推文。我们专门分析了非药物干预 (NPI) 和社会问题内容主题。公众对整个 COVID-19、NPI 和社会问题内容主题的跨时空意识和情绪存在显着差异。此外，还发现关键事件与对非营利机构和社会问题的突然情绪变化有关。这种新颖的基于 NLP 的 AI 工作流可以很容易地用于实时粒度内容主题和超出健康上下文的情绪信息监视。