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A Dissertation Submitted to the Faculty of The Data Science-Driven Crowd Intelligence and Its Business Applications Item Type text; Electronic Dissertation Authors Wei, Xuan Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 26/09/2021 05:08:03 Link to Item http://hdl.handle.net/10150/645751 DATA SCIENCE-DRIVEN CROWD INTELLIGENCE AND ITS BUSINESS APPLICATIONS by Xuan Wei __________________________ Copyright © Xuan Wei 2020 A Dissertation Submitted to the Faculty of the DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2020 ACKNOWLEDGEMENTS I hold a great appreciation for my dissertation committee members, Drs. Daniel Zeng, Wei Chen, Yong Ge, and Jason Pacheco, for their encouragement, inspiration, and guidance. I am especially grateful to my advisor, Dr. Daniel Zeng. The life lessons I learned about passion, professionalism, vision, attitude, teamwork, and many others, will have a persistent influence on my future career and life. I am also very grateful to all other faculty members in the MIS department and my coauthors, especially Dr. Zhu Zhang, for their scholarly communication and intellectual stimulation. Special thanks to my Ph.D. colleagues and friends in Tucson, especially Zhengchao Yang, Yang Gu, Yuanxia Li, Hao Liu, Zhipeng Chen, Jiayu Yao, Saiying Ge, Zisu Wang, Xinran Wang, Marni Mcdaniel, and many others, for their friendship and all these happy parties. Many thanks to senior students, especially Dr. Yongcheng Zhan, for their help and support. I also want to thank Dr. Jingyu Liu and Dr. Muhan Zhou for their help in life whenever needed. I also thank the MIS stuff members, Cinda Van Winkle and Dawn Bishop, for their logistic assistance. I also own my deepest gratitude to my loving wife, Dr. Mingyue Zhang, and family members for their company, support, and encouragement. I thank the National Institutes of Health (1R01DA037378) for providing the funding of my work. 3 DEDICATION This dissertation is dedicated to my wife and family 4 TABLE OF CONTENTS LIST OF FIGURES ...................................................................................................................... 8 LIST OF TABLES ...................................................................................................................... 10 ABSTRACT ................................................................................................................................. 11 1. INTRODUCTION................................................................................................................... 12 2. ESSAY I: MINING CROWD STANCE FROM SOCIAL MEDIA: A DEEP LEARNING APPROACH BASED ON INTERACTIVE ATTENTION .................................................... 19 2.1 Introduction ....................................................................................................................... 19 2.2 Literature Review ............................................................................................................. 23 2.2.1 Crowd Opinions in Social Media .............................................................................. 23 2.2.2 Stance Detection and Related Problems ................................................................... 25 2.2.3 Techniques of Stance Detection ................................................................................ 27 2.3 Method ............................................................................................................................... 29 2.3.1 Problem Definition.................................................................................................... 29 2.3.2 Psychological Motivation ......................................................................................... 30 2.3.3 Interactive Attention-Based Stance Detection (IASD) ............................................. 31 2.3.3.1 Embedding Layer ......................................................................................................... 32 2.3.3.2 Context Encoding Layer ............................................................................................... 33 2.3.3.3 Attention Layer ............................................................................................................. 33 2.3.3.4 Prediction Layer ........................................................................................................... 34 2.3.3.5 Model Training ............................................................................................................. 34 2.4 Empirical Evaluation ........................................................................................................ 35 2.4.1 Experimental Design ................................................................................................. 35 2.4.2 Experimental Results ................................................................................................ 37 2.5 Discussion and Conclusions ............................................................................................. 39 3. ESSAY II: HOW TO DESIGN NEXT-GENERATION LEARNING FROM CROWDS IN A PRINCIPLED WAY? AN INTERPRETABLE FRAMEWORK COMBINING DEEP LEARNING AND GRAPHICAL MODELS ........................................................................... 43 3.1 Introduction ....................................................................................................................... 43 3.2 Related Work .................................................................................................................... 47 3.2.1 Truth Inference in Crowdsourcing ............................................................................ 47 3.2.2 Deep Generative Models and Inferences .................................................................. 50 3.3 Hypothesis Development .................................................................................................. 51 3.4 Deep Generative Modeling Framework.......................................................................... 58 3.4.1 DARF ........................................................................................................................ 59 3.4.2 DARFC and S-DARFC............................................................................................. 62 3.4.3 DARFCD .................................................................................................................. 63 5 3.4.4 Multi-Class Extensions ............................................................................................. 65 3.5 Model Inference ................................................................................................................ 66 3.6 Empirical Evaluations ...................................................................................................... 74 3.6.1 Experimental Design ................................................................................................. 74 3.6.2 Hypothesis Testing.................................................................................................... 78 3.6.3 Comparison with Benchmark Models ...................................................................... 85 3.6.4 Analysis and Discussion ........................................................................................... 86 3.7 Conclusions ........................................................................................................................ 89 4. ESSAY III: COMBINING CROWD AND MACHINE INTELLIGENCE TO DETECT FALSE NEWS IN SOCIAL MEDIA ........................................................................................ 93 4.1 Introduction ....................................................................................................................... 93 4.2 Related Work .................................................................................................................... 97 4.2.1 Theoretical Foundations............................................................................................ 98 4.2.2 Definition of False News and Its Classification ....................................................... 99 4.2.3 Computational False News Detection in Social Media .......................................... 101 4.2.4 False News Studies Using Crowd Wisdom ............................................................ 104 4.2.5 Information Aggregation ........................................................................................ 105 4.3 A Crowd-Powered Framework for False News Detection .......................................... 107 4.3.1 Information Extraction ............................................................................................ 108 4.3.2 Unsupervised Bayesian Result Aggregation Model CLNAM ................................ 109 4.3.2.1 Challenges in Result Aggregation .............................................................................. 110 4.3.2.2 Technical Insights ....................................................................................................... 111 4.3.2.3 CLNAM Model .......................................................................................................... 112 4.3.2.4 Model Inference .........................................................................................................
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