Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach

Gene Moo Lee, Associate Professor, UBC Sauder School of Business

27 Aug 2021 Friday, 10:30 AM to 12:00 PM

Via Zoom

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model's power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.

Gene Moo Lee is an Associate Professor (with tenure) of Information Systems at UBC Sauder School of Business. He received his Ph.D. in Computer Science from UT Austin in 2015. His research program is to advance business analytics using AI approaches. His research has been published in top-tier journals such as MIS Quarterly, Information Systems Research, Journal of MIS, Journal of Business Ethics, and Journal of Cybersecurity as well as top-tier CS conferences such as ACM EC, ACM IMC, and IEEE INFOCOM. His research has been financially supported by 17 grants (e.g., U.S. National Science Foundation, Canada SSHRC, and multiple industry grants) with a total of $1.1 million. He received the AIS Early Career Award in 2019 and is an AIS Distinguished Member. He has extensive industry experience at Samsung, AT&T, Intel, and Goldman Sachs, has collaborated with various tech firms (e.g., Yahoo, IGAWorks, KISTI, KIRI, Canada Energy Regulator), and holds 11 patents in mobile technology.