Essays on social media, social influence, and social comparison
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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 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.
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