Search advertising, a popular method for online marketing, has been employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and experimentation, which may not be available to health authorities wishing to elicit such changes, especially when dealing with public health crises such as epidemic outbreaks. Here, we develop a framework, comprising two neural network models, that automatically generates ads. The framework first employs a generator model, which creates ads from web pages. These ads are then processed by a translation model, which transcribes ads to improve performance. We trained the networks using 114K health-related ads shown on Microsoft Advertising. We measure ad performance using the click-through rates (CTR). Our experiments show that the generated advertisements received approximately the same CTR as human-authored ads. The marginal contribution of the generator model was, on average, 28% lower than that of human-authored ads, while the translator model received, on average, 32% more clicks than human-authored ads. Our analysis shows that, when compared to human-authored ads, both the translator model and the combined generator + translator framework produce ads reflecting higher values of psychological attributes associated with a user action, including higher valence and arousal, and more calls to action. In contrast, levels of these attributes in ads produced by the generator model alone are similar to those of human-authored ads. Our results demonstrate the ability to automatically generate useful advertisements for the health domain. We believe that our work offers health authorities an improved ability to build effective public health advertising campaigns.

搜索广告是一种流行的在线营销方法，已被用来通过引发积极的行为改变来改善健康状况。但是，撰写有效的广告需要专业知识和实验，希望引起这种变化的卫生当局可能无法获得这些知识和实验，特别是在应对流行病等公共卫生危机时。在这里，我们开发了一个包含两个神经网络模型的框架，该框架可以自动生成广告。该框架首先采用生成器模型，该模型从网页创建广告。然后，由翻译模型处理这些广告，该模型会转录广告以提高效果。我们使用Microsoft Advertising上显示的114K与健康相关的广告来训练网络。我们使用点击率（CTR）来衡量广告效果。我们的实验表明，生成的广告所获得的点击率与人工创作的广告大致相同。生成器模型的边际贡献平均比人工制作的广告低28％，而翻译器模型平均获得的点击次数比人工制作的广告高32％。我们的分析表明，与人类创作的广告相比，翻译器模型和组合的生成器+翻译器框架所产生的广告均反映出与用户操作（包括更高的效价和唤醒）以及更多的号召性用语相关的心理属性的较高值。相反，仅由生成器模型生成的广告中这些属性的级别类似于人工创作的广告。我们的结果证明了能够自动为健康域生成有用的广告的能力。