Persuasion Strategies in Advertisements
Yaman Kumar, Rajat Jha, Arunim Gupta, Milan Aggarwal, Aditya Garg, Tushar Malyan, Ayush Bhardwaj, Rajiv Ratn Shah
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. 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 at https://midas-research.github.io/persuasion-advertisements/.
BibTeX
@article{Kumar_Jha_Gupta_Aggarwal_Garg_Malyan_Bhardwaj_Ratn Shah_Krishnamurthy_Chen_2023, title={Persuasion Strategies in Advertisements}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25076}, DOI={10.1609/aaai.v37i1.25076}, abstractNote={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. 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 at https://midas-research.github.io/persuasion-advertisements/.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Kumar, Yaman and Jha, Rajat and Gupta, Arunim and Aggarwal, Milan and Garg, Aditya and Malyan, Tushar and Bhardwaj, Ayush and Ratn Shah, Rajiv and Krishnamurthy, Balaji and Chen, Changyou}, year={2023}, month={Jun.}, pages={57-66} }