Be Careful What You Look for: Tagged Social Media Content Biases Brand Analyses

Abstract

User attention in social media has long shifted from text to images. These images often contain brand logos in various contexts. The rapidly proliferating amount of such brand imagery makes it challenging for marketing research and practice to keep track of how brands appear online. The most intuitive data collection approach is to search for brand tags (e.g., #heineken) and study all related images. However, less than 20% of branded user-generated content contains a brand tag, leaving the remaining 80% unconsidered. We use deep convolutional neural networks to analyze more than 200,000 brand image posts with and without brand tags, covering more than 150 brands. Pursuing a multi-method approach, we consistently find that brand tags are not assigned randomly by users. Specifically, users are more likely to add brand tags to images with higher logo presence and image quality. Moreover, brands have differential tag potential such that brands with higher levels of user interest feature a higher share of brand-tagged images. Conversely, objects on the image that compete with brand visibility result in fewer brand tags. These systematic differences also affect viewer response, with tagged real-world images obtaining higher purchase-intention ratings in a controlled lab setting. Overall, we provide converging evidence that analyzing only brand-tagged content paints a biased picture of visual brand presence and discuss how marketing research can address these sample selection issues.

Keywords: Machine Learning, Hashtags, Social Media, Image Analysis, User-Generated Content, Deep Learning