
Copyright by Qian Tang 2013 The Dissertation Committee for Qian Tang Certifies that this is the approved version of the following dissertation: Essays on Social Media, Social Influence, and Social Comparison Committee: Andrew B. Whinston, Supervisor Bin Gu John Mote Ashish Agarwal Wen Wen Essays on Social Media, Social Influence, and Social Comparison by Qian Tang, B.M.S.; M.M.; M.S. Econ. DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY The University of Texas at Austin August 2013 Dedicated to my parents. Acknowledgements I owe my deepest gratitude to my advisor, Dr. Andrew B. Whinston, who has taught me to conduct research projects that can make an impact and encouraged me to take challenges. I also would like to express my appreciation for the faculty members, Dr. Bin Gu, Dr. John Mote, Dr. Ashish Agarwal, and Dr. Wen Wen for their insightful comments that have helped provoke new thoughts on my research. It is my pleasure to thank the fellow doctoral students at the University of Texas at Austin for their support. Finally, this dissertation would not have been possible without the support of my family and friends. I would like to take this great opportunity to thank my husband, Brian Gu and my parents, Zhucai Tang and Guixiang Li, for their care, understanding, and encouragements along the way. v Essays on Social Media, Social Influence, and Social Comparison Qian Tang, Ph.D. The University of Texas at Austin, 2013 Supervisor: Andrew B. Whinston Social networking and social media technologies have greatly changed the way information is created and transmitted. Social media has made content contribution an efficient approach for individual brand building. With abundant user generated content and social networks, content consumers are constantly subject to social influence. Such social influence can be further utilized to encourage pro-social behavior. Chapter 1 examines the incentives for content contribution in social media. We propose that exposure and reputation are the major incentives for contributors. Besides, as more and more social media websites offer advertising-revenue sharing with some of their contributors, shared revenue provides an extra incentive for contributors who have joined revenue-sharing programs. We develop a dynamic structural model to identify a contributor’s underlying utility function from observed contribution behavior. We recognize the dynamic nature of the content-contribution decision—that contributors are forward-looking, anticipating how their decisions impact future rewards. Using data collected from YouTube, we show that content contribution is driven by a contributor’s desire for exposure, revenue sharing, and reputation and that the contributor makes decisions dynamically. Chapter 2 examines how social influence impact individuals’ content consumption decisions in social network. Specifically, we consider social learning and vi network effects as two important mechanisms of social influence, in the context of YouTube. Rather than combining both social learning and network effects under the umbrella of social contagion or peer influence, we develop a theoretical model and empirically identify social learning and network effects separately. Using a unique data set from YouTube, we find that both mechanisms have statistically and economically significant effects on video views, and which mechanism dominates depends on the specific video type. Chapter 3 studies incentive mechanism to improve users’ pro-social behavior based on social comparison. In particular, we aim to motivate organizations to improve Internet security. We propose an approach to increase the incentives for addressing security problems through reputation concern and social comparison. Specifically, we process existing security vulnerability data, derive explicit relative security performance information, and disclose the information as feedback to organizations and the public. To test our approach, we conducted a field quasi-experiment for outgoing spam for 1,718 autonomous systems in eight countries. We found that the treatment group subject to information disclosure reduced outgoing spam approximately by 16%. Our results suggest that social information and social comparison can be effectively leveraged to encourage desirable behavior. vii Table of Contents Abstract…………………………………………………………………………...vi List of Tables ......................................................................................................... xi List of Figures ...................................................................................................... xiii Chapter 1: Content Contribution for Revenue Sharing and Reputation in Social Media ....................................................................................................1 1.1 Introduction ............................................................................................1 1.2 Literature Review...................................................................................4 1.3 Research Context ...................................................................................6 1.4 Empirical Approach ...............................................................................8 1.4.1 Current-Period Utility Function ....................................................9 1.4.2 Forecasting and Dynamic Transition ..........................................12 1.4.3 Markov Decision Process ...........................................................17 1.5 Data ......................................................................................................21 1.51 Data Collection ...........................................................................21 1.5.2 Summary Statistics......................................................................24 1.5.3 Censored Data on Reputation .....................................................27 1.6 Estimation ............................................................................................28 1.6.1 Two-Stage Estimation Procedure ...............................................28 1.6.2 Identification ...............................................................................31 1.6.3 Results .........................................................................................32 1.6.4 Partners vs. Nonpartners .............................................................38 1.6.5 Alternative Ranking ....................................................................42 1.6.6 Alternative Reputation Measures ................................................43 1.7 Discussion and Conclusions ................................................................43 1.8 Future Research ...................................................................................46 viii Chapter 2: Distinguishing Social Learning from Network Effects in Social Media ............................................................................................................48 2.1 Introduction ..........................................................................................48 2.2 Literature Review.................................................................................51 2.3 A Theoretical Framework of Social Learning and Network Effects on YouTube ..............................................................................................53 2.3.1 A Model of Social Learning on YouTube ..................................53 2.3.2 A Model of Network Effects on YouTube .................................59 2.4 Data ......................................................................................................62 2.5 Empirical Framework ..........................................................................64 2.5.1 Identification of the Surprise ......................................................64 2.5.2 A Test of Social Learning ...........................................................67 2.5.3 A Test of Network Effects ..........................................................72 2.6 Application: How to Go Viral? ............................................................75 2.7 Conclusions ..........................................................................................78 Chapter 3: Improving Internet Security Through Social Information and Social Comparison .........................................................................................80 3.1 Introduction ..........................................................................................80 3.2 Literature Review.................................................................................84 3.2.1 Internet Security ..........................................................................84 3.2.2 Regulations on Information Disclosure ......................................85 3.2.3 The Economics of Internet Security ...........................................87 3.2.4 Social Comparison ......................................................................88 3.3 Field Quasi-Experiment .......................................................................90 3.3.1 Outgoing Spam ...........................................................................91 3.3.2 SpamRankings.net ......................................................................92 3.3.3 Quasi-Experimental Design ........................................................94 3.4 Data ......................................................................................................96
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