Essays on Online Platforms and Human-Algorithm Interaction

Abstract

This dissertation contains three chapters that analyze how algorithms on social media platforms influence the content that users engage with and how individuals incorporate algorithmic predictions in their decision-making. In Chapter 1, I study how engagement maximizing news feed algorithms on social media affect the credibility of news content with which users engage. This allows me to estimate the extent to which engagement-maximizing algorithms promote and incentivize low-quality content. In addition, I evaluate how the ranking algorithm itself can be designed to promote and encourage engagement with high quality content. In Chapter 2, I analyze how the introduction of a new non-personalized news feed impacts user engagement quantity, quality, and diversity on the Reddit platform. I find that this auxiliary feed increases the share of users that engage with news-related content and the diversity of engagement within news categories and within articles from publishers across the political spectrum increases as a result of the feed. In Chapter 3, in collaboration with Nikhil Agarwal, Tobias Salz, and Pranav Rajpurkar, we study human-AI collaboration using an information experiment with professional radiologists. Results show that providing (i) AI predictions does not always improve performance, whereas (ii) contextual information does. Radiologists do not realize the gains from AI assistance because of errors in belief updating – they underweight AI predictions and treat their own information and AI predictions as statistically independent.