Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach
Authors: Shin, Donghyuk; He, Shu; Lee, Gene Moo; Whinston, Andrew B.; Cetintas, Suleyman; Lee, Kuang-Chih
Journal: MIS Quarterly (2020)
<jats:p>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 illus…