Joint Effects of Recommender Systems and Network Structure on the Visibility of Content and Creators
2026-06-30 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors used a virtual social media model to study how different recommendation methods and network setups affect which content and creators get seen more. They found that popularity-based recommendations tend to concentrate visibility on a few pieces of content and their creators, while collaborative filtering spreads attention more evenly. The structure of the follower network influences who benefits most, often favoring socially popular creators under popularity rankings. Their work suggests that deciding who gets seen involves looking at many factors and that simulations can help understand this before launching new recommendation systems.
social media algorithmsrecommendation systemspopularity rankingcollaborative filteringnetwork topologyvisibility allocationagent-based simulationsocial networkscontent creators
Authors
Virginia Morini, Valentina Pansanella, Luca Pappalardo, Dino Pedreschi, Giulio Rossetti
Abstract
Social media algorithms allocate users' visibility by ranking content within their social networks. Yet, how recommendation logic and network structure jointly shape visibility across content and creators remains largely understudied. In this work, we tackle this question through agent-based simulations using YSocial, a social media virtual twin, in which agents interact under 7 recommendation strategies and 2 network topologies. We find that recommender logic sets the visibility regime: popularity creates a reinforcement loop in which early reactions increase later exposure, concentrating visibility on a small subset of content and limiting creator visibility to those whose content enters this loop, while collaborative filtering distributes visibility broadly across the active catalogue and user base. When the follower graph shapes candidate selection, network structure changes the direction of inequality: under popularity ranking, creator-level concentration becomes comparable to global popularity, but visibility is systematically redirected toward creators who are already socially popular. Network topology modulates the magnitude of these effects without changing their qualitative ordering. These results show that visibility allocation should be evaluated across content, creators, network position, and temporal reinforcement, and that controlled simulations can help test how feed design distributes visibility before deployment.