Abstract

Visual social media have emerged as an essential brand communication channel for advertisers and brands. The active use of hashtags has enabled advertisers to identify customers interested in their brands and better understand their consumers. However, some users post brand-incongruent content—for example, posts composed of brand-irrelevant images with brand-relevant hashtags. Such visual information mismatch can be problematic because it hinders other consumers’ information search processes and advertisers’ insight generation from consumer-initiated social media data. This study aims to characterize visually mismatched content in brand-related posts on Instagram and builds a visual information mismatch detection model using computer vision. We propose a machine-learning model based on three cues: image, text, and metadata. Our analysis shows the effectiveness of deep learning and the importance of combining text and image features for mismatch detection. We discuss the advantages of machine-learning methods as a novel research tool for advertising research and conclude with implications of our findings.

摘要

视觉社交媒体已经成为广告商和品牌的重要品牌传播渠道。主题标签的积极使用使广告商能够识别出对其品牌感兴趣的客户，并更好地了解其消费者。但是，某些用户会发布与品牌无关的内容，例如，由与品牌无关的图像和与品牌相关的标签组成的帖子。这种视觉信息不匹配可能会引起问题，因为它会阻碍其他消费者的信息搜索过程以及广告客户从消费者发起的社交媒体数据中产生洞察力。这项研究旨在表征Instagram品牌相关帖子中视觉不匹配的内容，并使用计算机视觉建立视觉信息不匹配检测模型。我们提出了一种基于三个线索的机器学习模型：图像，文本和元数据。我们的分析显示了深度学习的有效性以及将文本和图像特征组合在一起以进行失配检测的重要性。我们讨论了机器学习方法作为广告研究的一种新颖研究工具的优势，并得出结论。