We use the following 12 strategies as our persuasion strategy set: Social Identity, Concreteness, Anchoring and Comparison, Overcoming Reactance, Reciprocity, Foot-in-the-Door, Authority, Social Impact, Anthropomorphism, Scarcity, Social Proof, and Unclear. There are 1002 videos, each annotated with one or more from the following persuasion strategies.
Link : https://drive.google.com/drive/folders/1rATHvwd4sOYB363ijGObAkev_Z3uqFPn?usp=share_link
If you use this dataset, please cite the following works:
@article{Kumar_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}}
@article{bhattacharya2023video,
title={A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot},
author={Bhattacharya, Aanisha and Singla, Yaman K and Krishnamurthy, Balaji and Shah, Rajiv Ratn and Chen, Changyou},
journal={arXiv preprint arXiv:2305.09758},
year={2023}
}