Social Media Moderations, User Ban, and Content Generation: Evidence from Zhihu

Social media platforms have evolved as major outlets for many entities to distribute and consume information. The content on social media sites, however, are often considered inaccurate, misleading, or even harmful. To deal with such challenges, the platforms have developed rules and guidelines to moderate and regulate the content on their sites. In this study, we explore user banning as a moderation strategy that restricts, suspends, or bans a user who the platform deems as violating community rules from further participation on the platform for a predetermined period of time. We examine the impact of such moderation strategy using data from a major Q&A platform. Our analyses indicate that user banning increases a user’s contribution after the platform lifts the ban. The magnitude of the impact, however, depends on the user’s engagement level with the platform. We find that the increase in contributions is smaller for a more engaged user. Additionally, we find that the quality of the user-generated content (UGC) decreases after the user ban is lifted. Our research is among the first to empirically evaluate the effectiveness of platform moderations. The findings have important implications for platform owners in managing the content on their sites

Essays on the Economics of Electronic Commerce and Social Networks

This dissertation studies the economics of electronic commerce and social networks. The first essay, "Market Mechanisms in Online Crowdfunding," systematically compares auctions with a platform-mandated posted-price mechanism in online crowdfunding. We exploit a regime change from auctions to platform-mandated posted prices on a debt-based crowdfunding platform. We develop a game-theoretic model that yields empirically testable hypotheses, then test them using detailed transactions data. Consistent with our hypotheses, we find that after the regime change, loans are funded with higher probability and higher interest rates. More important, all else equal, loans funded under the posted-price regime are more likely to default. While the posted prices may be faster than auctions that rely on the "crowd" to discover prices, auctions are not necessarily inferior in terms of overall social welfare. The essay, "'Smart Money': Institutional Investors in Online Crowdfunding," studies institutional investors and their interactions with retail investors in online crowdfunding. Given their expertise, institutional investors are often referred to as "smart money" in financial markets, in the sense that these professional have selection ability and receive higher returns from their securities. We study whether institutional investors are indeed better able to screen borrowers in the marketplace than, and have any significant impacts on the behaviors of, retail investors. We find that although institutional investors indeed behave differently in terms of portfolio size and diversification strategies, their portfolios do not necessarily outperform those of retail investors. Institutional investors' bids have significant influence on the bidding strategies of retail investors, as well as final transaction outcomes. We find that this phenomenon is driven by the designation of "institutional investors" rather than just the size of their portfolios. The last essay, "For Whom to Tweet? A Study of a Large-Scale Social Network," studies the effects of peer-groups sizes on individuals' contributions to public goods - tweets - in a large-scale and influential online social network. We attribute the highly-skewed distribution of tweets, which is observed from the network, to an individual's conflicting incentives of free-riding and maximizing social influence. We exploit the asymmetry of an individual's peer groups (followers and followees, groups of people following and being followed by the individual respectively) to disentangle these incentives, and devise empirical strategies to deal with the endogenous formation of one's peer groups. We find a larger group of followees leads an individual to tweet less, while a larger group of followers leads an individual to tweet more. With randomly generated 1% new links the total tweets will increase by 25% as the estimated follower effects dominate the followee effects

Platform Mispricing and Lender Learning in Peer-to-Peer Lending

We document how online lenders exploit a flawed, new pricing mechanism in a peer-to-peer lending platform: Prosper.com. Switching from auctions to a posted-price mechanism in December 2010, Prosper assigned loan listings with different estimated loss rates into seven distinctive rating grades and adopted a single price for all listings with the same rating grade. We show that lenders adjusted their investment portfolios towards listings at the low end of the risk spectrum of each Prosper rating grade. We find heterogeneity in the speed of adjustment by lender experience, investment size, and diversification strategies. It took about 16 - 17 months for an average lender to take full advantage of the "cherry-picking" opportunity under the single-price regime, which is roughly when Prosper switched to a more flexible pricing mechanism.12 month embargo; published online: 10 October 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Experts vs. Nonexperts in Online Crowdfunding Markets

The growth of crowdfunding markets that include both expert and nonexpert investors will soon accelerate due to recent changes in Securities Exchange Commission (SEC) regulations. Prior work has suggested that nonexperts (1) may benefit from experts’ participation via mimicking their trades, but (2) will also face a cost, as experts crowding nonexperts out of the best opportunities will ensure that nonexperts will suffer lower returns than experts. Traditional economic theory holds that the crowding effect means that the relative importance of nonexperts in the market will decline over time until they become unimportant. Exploiting a unique period in one crowdfunding market (Prosper.com) that allowed us to directly estimate the net cost of competing with better-informed experts, we found that the net negative effects of expert participation on nonexperts are small. We used simulations to both better understand (1) the market characteristics and crowdfunding platform choices that influence experts’ and nonexperts’ returns, their return gap, and the extent to which nonexperts are better or worse off relative to a market without expert participation, and (2) the factors that may contribute to the small expert/nonexpert Prosper return gap