ADVERTISEMENT ALLOCATION AND TRUST MECHANISMS DESIGN IN SOCIAL NETWORKS

Social network sites (SNS), such as Facebook, Google+ and Twitter, have attracted hundreds of millions of users daily since their appearance. Within SNS, users connect to each other, express their identity, disseminate information and form cooperation by interacting with their connected peers. The increasing popularity and ubiquity of SNS usage and the invaluable user behaviors and connections give birth to many applications and business models. We look into several important problems within the social network ecosystem. The first one is the SNS advertisement allocation problem. The other two are related to trust mechanisms design in social network setting, including local trust inference and global trust evaluation. In SNS advertising, we study the problem of advertisement allocation from the ad platform's angle, and discuss its differences with the advertising model in the search engine setting. By leveraging the connection between social networks and hyperbolic geometry, we propose to solve the problem via approximation using hyperbolic embedding and convex optimization. A hyperbolic embedding method, \hcm, is designed for the SNS ad allocation problem, and several components are introduced to realize the optimization formulation. We show the advantages of our new approach in solving the problem compared to the baseline integer programming (IP) formulation. In studying the problem of trust mechanisms in social networks, we consider the existence of distrust (i.e. negative trust) relationships, and differentiate between the concept of local trust and global trust in social network setting. In the problem of local trust inference, we propose a 2-D trust model. Based on the model, we develop a semiring-based trust inference framework. In global trust evaluation, we consider a general setting with conflicting opinions, and propose a consensus-based approach to solve the complex problem in signed trust networks.

NONDISCLOSURE – A GOOD NEWS SIGNAL?

I examine the implications of nondisclosure in a setting where there is a credible signal as to the proprietary nature of the undisclosed information. Specifically, I investigate the market and analysts' response to firms’ application to the Securities and Exchange Commission (SEC) for a confidential treatment order (CTO), which allows firms to redact required disclosures from SEC filings when the redacted information is proprietary. I find that the market and analysts react favorably to the voluntary nondisclosure of proprietary information using the SEC confidential treatment process. Market and analysts reactions are more favorable to the redaction of information that is more likely to have proprietary value, such as information related to research and development. In addition, I show that the redacting firms experience superior accounting performance compared to their peers in the years following the redaction, consistent with the market and analysts’ response to the redaction. However, I find that analysts engage in more intense private information search in response to a CTO redaction. This finding suggests that, although a CTO redaction can signal the nature of undisclosed information, analysts believe that the signal is not fully revealing of the economic magnitude of the undisclosed information. Overall, this study’s findings indicate that a firm's willingness to submit to the CTO approval process serves as a credible signal of the proprietary nature of the withheld information. The results of this study suggest a possible role for a credible signaling channel to facilitate communication between insiders and outsiders regarding the nature of withheld information.