Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Persuasion Strategies in Advertisements: Dataset, Modeling, and Baselines

Yaman Singla
* ORCID logo ,
Rajat Jha
,
Arunim Gupta
,
Milan Aggarwal
,
Aditya Garg
,
Ayush Bhardwaj
,
Tushar -
,
Balaji Krishnamurthy
,
Rajiv Ratn Shah
,
Changyou Chen
Version 1 : Received: 20 August 2022 / Approved: 22 August 2022 / Online: 22 August 2022 (07:51:33 CEST)

How to cite: Singla, Y.; Jha, R.; Gupta, A.; Aggarwal, M.; Garg, A.; Bhardwaj, A.; -, T.; Krishnamurthy, B.; Ratn Shah, R.; Chen, C. Persuasion Strategies in Advertisements: Dataset, Modeling, and Baselines. Preprints 2022, 2022080377. https://doi.org/10.20944/preprints202208.0377.v1 Singla, Y.; Jha, R.; Gupta, A.; Aggarwal, M.; Garg, A.; Bhardwaj, A.; -, T.; Krishnamurthy, B.; Ratn Shah, R.; Chen, C. Persuasion Strategies in Advertisements: Dataset, Modeling, and Baselines. Preprints 2022, 2022080377. https://doi.org/10.20944/preprints202208.0377.v1

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 per- suasion 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/

Keywords

persuasion; persuasion strategies; influence; advertising; advertisements, adobe

Subject

Computer Science and Mathematics, Computer Science

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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