Persuasion Strategies in Advertisements
This work addresses the lack of benchmark datasets for persuasion strategy prediction in computer vision, benefiting researchers in propaganda, social psychology, and marketing, though it is incremental in building on existing literature.
The authors tackled the problem of computational modeling of persuasion in advertisements by creating the first benchmark dataset annotated with persuasion strategies and developing a multi-task attention fusion model for prediction, achieving analysis of strategies across demographics in a case study with 1600 advertising campaigns.
Modeling what makes an advertisement persuasive, i.e., eliciting the desired response from consumer, is critical to the study of propaganda, social psychology, and marketing. Despite its importance, computational modeling of persuasion in computer vision is still in its infancy, primarily due to the lack of benchmark datasets that can provide persuasion-strategy labels associated with ads. Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning, where we design a multi-task attention fusion model that can leverage other ad-understanding tasks to predict persuasion strategies. Further, we conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies where we use our model's predictions to analyze which strategies work with different demographics (age and gender). The dataset also provides image segmentation masks, which labels persuasion strategies in the corresponding ad images on the test split. We publicly release our code and dataset https://midas-research.github.io/persuasion-advertisements/.