论文标题
我们应该发推文吗？用于预测Twitter上公共卫生消息传递的生成响应建模
Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter
论文作者
论文摘要
人们在社交媒体上对公共卫生组织的消息传递的反应方式可以洞悉公众对关键健康问题的看法，尤其是在Covid-19等全球危机期间。对于美国疾病控制与预防中心（CDC）或世界卫生组织（WHO）等高影响力组织（例如，了解这些看法如何影响消息传递对卫生政策建议的接收）可能是有价值的。我们从Twitter收集了两个与Covid-19和疫苗有关的公共卫生消息的数据集及其回答，并引入了一种预测方法，该方法可用于探索潜在的此类信息的接收。具体而言，我们利用生成模型（GPT-2）直接预测可能的未来响应，并证明如何使用它来优化对重要健康指导的预期接收。最后，我们介绍了一种新的评估方案，并具有广泛的统计测试，这使我们能够得出结论，我们的模型捕获了实际公共卫生反应中发现的语义和情感。
The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.
