论文标题
广告中的说服策略
Persuasion Strategies in Advertisements
论文作者
论文摘要
建模是什么使广告有说服力的原因，即引起消费者的所需响应，对宣传，社会心理学和营销的研究至关重要。尽管它很重要，但计算机视觉中说服力的计算建模仍处于起步阶段，这主要是由于缺乏基准数据集，这些数据集可以提供与ADS相关的说服力 - 策略标签。由社会心理学和营销中的说服文学的促进，我们引入了广泛的说服策略词汇量，并建立了以说服策略注释的第一个AD图像语料库。然后，我们通过多模式学习制定说服策略预测的任务，在该任务中，我们设计了一个多任务注意融合模型，该模型可以利用其他广告理解的任务来预测说服力策略。此外，我们对30家财富500家公司的1600个广告活动进行了真实的案例研究，我们使用模型的预测来分析哪些策略与不同的人口统计学（年龄和性别）一起使用。该数据集还提供图像分割掩码，该蒙版在测试拆分上标记了相应的AD图像中的说服策略。我们公开发布代码和数据集https://midas-research.github.io/persuasion-avertisements/。
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/.
