[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84542-en":3,"doc-seo-84542-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84542,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Joint Effects of Recommender Systems and Network Structure on the Visibility of Content and Creators","Social media algorithms allocate user visibility by ranking content within social networks, yet how recommendation logic and network structure jointly shape visibility of both content and creators remains insufficiently studied. This work uses agent-based simulations with YSocial, modeling interactions under seven recommendation strategies and two network topologies. Results show recommender logic defines the visibility regime: popularity induces reinforcement loops that concentrate visibility, while collaborative filtering spreads it across the active catalogue. Network structure shifts inequality direction at the creator level and changes effect magnitudes without altering their qualitative ordering.","JOINT EFFECTS OF RECOMMENDER SYSTEMS AND NETWORK STRUCTURE ON THE VISIBILITY OF CONTENT AND CREATORS  \narXiv :2607 .00258v 1 [ cs . SI] 30 Jun 2026  \nVirginia Morini  \nDepartment of Computer Science, University of Pisa ISTI-CNR, National Research Council of Italy Pisa, Italy [virginia.morini@di.unipi.it](virginia.morini@di.unipi.it)  \nValentina Pansanella  \nISTI-CNR, National Research Council of Italy Pisa, Italy [valentina.pansanella@isti.cnr.it](valentina.pansanella@isti.cnr.it)  \nLuca Pappalardo  \nISTI-CNR, National Research Council of Italy Scuola Normale Superiore Pisa, Italy [luca.pappalardo@isti.cnr.it](luca.pappalardo@isti.cnr.it)  \nDino Pedreschi  \nDepartment of Computer Science, University of Pisa Pisa, Italy [dino.pedreschi@unipi.it](dino.pedreschi@unipi.it)  \nGiulio Rossetti  \nISTI-CNR, National Research Council of Italy  \nPisa, Italy  \n[giulio.rossetti@isti.cnr.it](giulio.rossetti@isti.cnr.it)  \nABSTRACT  \nSocial 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.  \nWhen 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.  \nNetwork 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.  \n1 Introduction  \nRecommender systems have a central role in the attention economy, allocating visibility across competing items and creators [27, 4] . This is especially evident on social media platforms, where algorithmically curated feeds have become the main access point to content, and a key instrument for user engagement and retention [34] .  \nIn this ecosystem, recommendation strategies determine which content and creators are made visible, how often they are shown, and how exposure is distributed across the platform [31] . This allocation of exposure is not neutral: by consistently ranking some content above others, recommender systems can amplify the visibility of users, topics or opinions, while reducing the visibility of others [30] . Prior work has linked the use of recommendation strategies to the emergence of (unintended) outcomes such as selective exposure, filter bubbles, polarization, and radicalization [5, 7, 26, 29] . At the same time, empirical evidence is mixed and context-dependent: recommendations can concentrate  \nattention on prominent accounts or high-popularity content [30, 57], but it can also increase aggregate diversity in some settings [58] or deamplify niche content when user utility is taken into account [50] . Similar effects are found across different environments [44, 43]: in online retail platforms, recommenders favor popular products, brands, or sellers [33]; in location-based platforms, they favor already popular points of interest [36] . Therefore, the central question isnot whether recommender systems always amplify visibility, but how diff","cbCaij8vO51Mh6g6","https://ap.wps.com/l/cbCaij8vO51Mh6g6","pdf",8597754,1,24,"English","en",105,"# Introduction\n## Recommendation strategies and visibility allocation\n## Social networks as a layer of selection\n## Research gap and study objectives","[{\"question\":\"What question does the document investigate about social media algorithms?\",\"answer\":\"How recommendation logic and social network structure jointly determine visibility for both content and creators in social media feeds.\"},{\"question\":\"What method and environment are used to study these effects?\",\"answer\":\"Agent-based simulations using YSocial, with agents interacting under seven recommendation strategies and two network topologies.\"},{\"question\":\"How do popularity-based and collaborative-filtering strategies differ in visibility outcomes?\",\"answer\":\"Popularity ranking can create reinforcement loops concentrating visibility on a small subset of content and limiting creator visibility, whereas collaborative filtering distributes visibility more broadly across the catalogue and user 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