【Topic】
Visual Listening In: Extracting Brand Image Portrayed on Social Media
【Online ID】
SuiMeeting: 7214231443
【Abstract】
Images are close to surpassing text as the medium of choice for online conversations. They convey rich information about the consumption experience, attitudes, and feelings of the user. In this paper, we propose a “visual listening in” approach (i.e., mining visual content posted by users) to measure how brands are portrayed on social media. We develop BrandImageNet, a multi-label deep convolutional neural network model, to predict the presence of perceptual brand attributes in the images consumers post online. We validate BrandImageNet model performance using human judges and find a high degree of agreement between our model and human evaluations of images. We apply the BrandImageNet model to brand-related images posted on social media to extract brand portrayal based on model predictions for 56 national brands in the apparel and beverages categories. We find a strong link between brand portrayal in consumer-created images and consumer brand perceptions collected through traditional survey tools. Firms can use the BrandImageNet model to automatically monitor their brand portrayal in real time and better understand consumer brand perceptions and attitudes toward their and competitors’ brands.
【Speaker】
Prof. LIU Liu
Liu Liu is an Assistant Professor of Marketing in the Leeds School of Business at the University of Colorado Boulder. She received her PhD in Marketing from New York University Stern School of Business. She also holds a BE from Tsinghua University and MS from Carnegie Mellon University, both in Computer Science. Prior to starting her academic career, she worked at Google for three years as a Software Engineer, doing large-scale machine learning for the AdSense system.Broadly speaking, Liu’s research lies in the intersection of marketing and machine learning. She is interested in developing new methodologies and tailoring state-of-the-art machine learning and deep learning methods to marketing problems, in areas such as visual marketing, branding, product design and innovation, social media, and consumer choice modeling. Her previous research on measuring brand perceptions from consumer-generated images on social media has won her several awards, including the 2018 Marketing Section of the American Statistical Association's Doctoral Research Award, a finalist for John D.C. Little award, a finalist for Frank M. Bass best dissertation-based paper award, and a finalist for John A. Howard/American Marketing Association doctoral dissertation award. Liu is currently on the editorial review board of Marketing Science.
