Visual Listening In: Extracting Brand Image Portrayed on Social Media
50 Pages Posted: 6 Jun 2017 Last revised: 8 Mar 2019
Date Written: October 1, 2018
Marketing academics and practitioners recognize the importance of monitoring consumer online conversations about brands. The focus so far has been on user-generated content in the form of text. However, images are on their way to surpassing text as the medium of choice for social conversations. In these images, consumers often tag brands. We propose a ``visual listening in" approach (i.e., mining visual content posted by users) to measure how brands are portrayed on social media. Our approach consists of two stages. We first use two supervised machine-learning methods, support vector machine and deep convolutional neural networks, to measure brand attributes (glamorous, rugged, healthy, fun) from images. We then apply the classifiers to brand-related images posted on social media to measure what consumers are visually communicating about brands. We study 56 brands in the apparel and beverages categories, and compare their portrayal in consumer-created images with images on the firm's official Instagram account, as well as with consumer brand perceptions measured in a national brand survey. The three measures exhibit convergent validity. This finding indicates that consumers' photos on social media contain valuable brand-image information, which our method is able to pick up.
Keywords: Social Media, Visual Marketing, Brand Perceptions, Computer Vision, Machine Learning, Deep Learning, Transfer Learning, Big Data
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