Persuasion Strategies in Advertisements

TL;DR

This paper introduces the first annotated ad image dataset with persuasion strategies, and proposes a multi-modal learning model to predict these strategies, enabling analysis of their effectiveness across demographics.

Contribution

It provides an extensive persuasion strategy vocabulary, a new annotated dataset, and a multi-task model for predicting persuasion strategies in advertisements.

Findings

Model achieves accurate persuasion strategy prediction.

Analysis reveals different strategies resonate with various demographics.

Dataset includes segmentation masks for detailed strategy localization.

Abstract

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/.