Algorithmic copywriting: automated generation of health-related advertisements to improve their performance - Youngmann, Brit; Yom-Tov, Elad; Gilad-Bachrach, Ran; Karmon, Danny (Information Retrieval Journal, 2021)
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.