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Activity Number: 384 - Artificial Intelligence Meets Behavioral Science: Innovations in Discovering and Leveraging Nudges
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Marketing
Abstract #300493
Title: Visual Listening In: Extracting Brand Image Portrayed on Social Media
Author(s): Liu Liu* and Daria Dzyabura and Natalie Mizik
Companies: University of Colorado Boulder - Leeds School of Business and New York University Stern School of Business and University of Washington - Foster School of Business
Keywords: social media; visual marketing; brand perception; machine learning; deep learning; computer vision
Abstract:

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 deep convolutional neural networks to measure brand attributes (e.g., glamorous, rugg) 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 (apparel and beverages), 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.


Authors who are presenting talks have a * after their name.

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