Visual Listening In: Extracting Brand Image Portrayed on Social Media-市场营销系|光华管理学院


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Visual Listening In: Extracting Brand Image Portrayed on Social Media


Marketing Seminar2017-26

Title: Visual Listening In: Extracting Brand Image Portrayed on Social Media

Speaker: Liu Liu, New York University

Time: Friday, 17 November, 14:00-16:00

Location: Room 217, Guanghua Building 2


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 and depict their experience with the brands. We propose a “visual listening in” approach to measuring how brands are portrayed on social media (Instagram) by mining visual content posted by users, and show what insights brand managers can gather from social media by using this approach. Our approach consists of two stages. We first use two supervised machine learning methods, support vector machine classifiers 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. Although the three measures exhibit convergent validity, we find key differences between how consumers and firms portray the brands on visual social media, and how the average consumer perceives the brands.


Liu Liu, Ph.D. Candidate in New York University, her research interests are:

Ø Substantive: Visual marketing, Branding, Product design, Choice modeling, Social media

Ø Methodology: Machine learning, Deep learning, Natural language processing, Computer vision

Your participation is warmly welcomed!



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