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MIS Quarterly Vol. 44 No. 4 2020

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

Donghyuk Shin1; Shu He2; Gene Moo Lee3; Andrew B. Whinston4; Suleyman Cetintas5; Kuang-Chih Lee6

1 W. P. Carey School of Business, Arizona State University, Tempe, AZ 85281 U.S.A. · 2 School of Business, University of Connecticut, 2100 Hillside Road, Storrs, CT 06269 U.S.A. · 3 Sauder School of Business, The University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2 Canada · 4 McCombs School of Business, The University of Texas at Austin, 2110 Speedway, Austin, TX 78705 U.S.A. · 5 Advertising Science, Yahoo! Research, 701 First Avenue, Sunnyvale CA 94089 U.S.A. · 6 Marketplace Optimization, Alibaba Group, 400 S. El Camino Real, San Mateo, CA 94402 U.S.A.

Abstract

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.

DOI
10.25300/misq/2020/14870
Volume
44
Issue
4
Pages
1459-1492
Language
en
Sources
crossref openalex

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