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

MIS Quarterly 44(4), pp. 1459-1492.

Posted: 26 Aug 2016 Last revised: 27 Oct 2021

See all articles by Donghyuk Shin

Donghyuk Shin

Arizona State University (ASU) - Department of Information Systems

Shu He

University of Connecticut - Department of Operations & Information Management

Gene Moo Lee

University of British Columbia (UBC) - Sauder School of Business

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management

Suleyman Cetintas

Yahoo! - Yahoo! Research Labs

Kuang-Chih Lee

Yahoo! - Yahoo! Research Labs

Date Written: October 12, 2020

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.

Keywords: semantic content analysis, social media, online advertisement, customer engagement, deep learning, topic modeling, Big Data, representation learning, machine learning, Tumblr

Suggested Citation

Shin, Donghyuk and He, Shu and Lee, Gene Moo and Whinston, Andrew B. and Cetintas, Suleyman and Lee, Kuang-Chih, Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach (October 12, 2020). MIS Quarterly 44(4), pp. 1459-1492., Available at SSRN: https://ssrn.com/abstract=2830377 or http://dx.doi.org/10.2139/ssrn.2830377

Donghyuk Shin (Contact Author)

Arizona State University (ASU) - Department of Information Systems ( email )

Tempe, AZ
United States

Shu He

University of Connecticut - Department of Operations & Information Management ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Gene Moo Lee

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada

Andrew B. Whinston

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States
512-471-8879 (Phone)

Suleyman Cetintas

Yahoo! - Yahoo! Research Labs ( email )

Santa Clara, CA 95054
United States

Kuang-Chih Lee

Yahoo! - Yahoo! Research Labs ( email )

Santa Clara, CA 95054
United States

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