Peer Reviewed

Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.

The goal of influence maximization is to select a set of seed users that will optimally diffuse information through a network. In this paper, we study how applying traditional influence maximization algorithms affects the balance between different audience categories (e.g., gender breakdown) who will eventually be exposed to a message. More specifically, we investigate how structural homophily (i.e., the tendency to connect to similar others) and influence diffusion homophily (i.e., the tendency to be influenced by similar others) affect the balance among the activated nodes. We find that even under mild levels of homophily, the balance among the exposed nodes is significantly worse than the balance among the overall population, resulting in a significant disadvantage for one group. To address this challenge, we propose an algorithm that jointly maximizes the influence and balance among nodes while still preserving the attractive theoretical guarantees of the traditional influence maximization algorithms. We run a series of experiments on multiple synthetic and four real-world datasets to demonstrate the effectiveness of the proposed algorithm in improving the balance between different categories of exposed nodes.