@article{6b5428c10bf240a8b567739c09eba332,
title = "Enhancing social media analysis with visual data analytics: A deep learning approach",
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 = "Deep learning, Image-text similarity, Machine learning, Prediction, Social media, Visual data analytics, Word embedding",
author = "Donghyuk Shin and Shu He and Lee, {Gene Moo} and Whinston, {Andrew B.} and Suleyman Cetintas and Lee, {Kuang Chih}",
note = "Funding Information: Shu He is an assistant professor at the Department of Operations and Information Management, School of Business, University of Connecticut. She earned her Ph.D. in Economics from the University of Texas at Austin. Shu{\textquoteright}s research interests include social media, platform, online advertising, and cyber security. Her work has appeared in Information Systems Research, MIS Quarterly, and Journal of Cybersecurity. She has received grants from National Science Foundation and NET Institute to support her research. Funding Information: We thank the senior editor, associate editor, and two reviewers for their guidance and constructive feedback during the review process. We appreciate for the helpful suggestions and comments from the audience at the 2015 WeB and WITS conferences and 2016 INFORMS, CIST, DSI, Texas FreshAIR, and UKC conferences as well as seminar participants at the Arizona State University, Chung-Ang University, Hanyang University, Korea University, Kyungpook National University, Kyung Hee University, Seoul National University, Simon Fraser University, Sungkyunkwan University, University of British Columbia, University of Connecticut, University of North Texas, University of Texas at Arlington, University of Utah, and Yonsei University. For helpful feedback, the authors greatly thank Wayne Hoyer and Sang Pil Han. We also thank Myunghwan Lee for his help in converting our LaTeX manuscript to Microsoft Word format. Publisher Copyright: {\textcopyright} 2020 University of Minnesota. All rights reserved.",
year = "2020",
month = oct,
day = "25",
doi = "10.25300/MISQ/2020/14435",
language = "English (US)",
volume = "44",
pages = "1459--1492",
journal = "MIS Quarterly: Management Information Systems",
issn = "0276-7783",
publisher = "Management Information Systems Research Center",
number = "4",
}