Jiding Zhang

• Jiding Zhang, Ken Moon, Senthil Veeraraghavan (Under Revision), Does Fake News Create Echo Chambers?. Abstract

  Platforms have come under criticism from regulatory agencies, policymakers, and media scholars for the unfettered spread of fake news online. A key concern is that, as fake news becomes prevalent, individuals may fall into online “echo chambers” that predominantly expose them only to fake news. Using a dataset reporting 30,995 individual households’ online activity, we empirically examine the reach of false news content and whether echo chambers exist. We find that the population is widely exposed to online false news. However, echo chambers are minimal, and the most avid readers of false news content regularly expose themselves to mainstream news sources. Using a natural experiment occurring on a major social media platform, we find that being exposed to false news content causes households to increase their exposure to countervailing mainstream news (by 9.1% in the experiment). Hence, a naive intervention that reduces the supply of false news sources on a platform also reduces the overall consumption of news. Based on a structural model of household decisions whether to diversify their online news sources, we prescribe how platforms should moderate false news content. We find that platforms can further reduce the size of echo chambers (by 12-18%) by focusing their content moderation efforts on the households that are most susceptible to consuming predominantly false news, instead of the households most deeply exposed to false news.

• Amandeep Singh, Jiding Zhang, Senthil Veeraraghavan (Working), Fulfillment by Platform: Implications for Upstream Market Power.
• Jiding Zhang, Sergei Savin, Senthil Veeraraghavan (Under Revision), Revenue Management in Crowdfunding. Abstract

  We develop a model of crowdfunding dynamics that maximizes revenue for a given fundraising campaign by optimizing both the pledge level sought from donors or backers and the duration of the campaign. Our model aligns with the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. Using our model, we calibrate the revenue lost from using pre-specified pledge levels or campaign durations. We show that under the optimal design, the pledge level sought decreases as the goal of a campaign increases, with a more pronounced effect for both very low and very high campaign goals. We further demonstrate how uncertainty in pledge accumulation improves campaign revenue and aids campaign success. In particular, we show that campaigns with high goals benefit from highly uncertain environments more than campaigns with low goals.

• Ken Moon, Jiding Zhang, Elena Belavina, Karan Girotra (Working), Matching in Labor Marketplaces: The Role of Experiential Information. Abstract

  Online labor marketplaces match workers into short-term jobs. Whereas the quality of a match often hinges on the quality of the worker, in many markets the buyers of services must directly interact with workers in order to learn worker quality. Mounting evidence further suggests that reputational systems frequently fail to capture and spread such learning. We study the platform intermediary’s problem of matching workers to jobs when worker quality must be learned experientially. Platforms face a choice between experimenting to accelerate experiential learning and maximizing short-term match quality. They then face two additional hurdles. First, until experiential learning about individual workers is elicited from buyers (e.g., submitted through ratings) or deduced by the platform, it remains buyers’ private information. In particular, platforms may need to infer worker quality from observing buyers’ hiring decisions. Second, services may need to be tailored to buyers. Then, experimenting on matches incurs efficiency losses, since breaking existing matches imposes renewed setup costs. Using data from two primary job categories on a major online freelancer platform, our empirical study of 1.2M hiring decisions finds that experiential learning impacts hiring decisions significantly more than reputational information. We use structural estimation to evaluate the platform’s choice of matching policies. The best-performing policies increase buyer welfare by up to 45-47% of gross revenue by balancing a priority for repeat work against experimentation with new workers. Greedy policies under-explore and therefore under-perform by 18.9% and 8.7% in the two markets we study.

Instructor

• Wharton Math Camp (PhD), Summer 2019

Teaching Assistant

• OIDD 615, Operations Strategy (MBA elective), Spring 2019
• OIDD 101, Introduction to Operations and Information Management (undergraduate core), Spring 2017, Spring 2018