{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/minimizing-polarization-and-disagreement-in","title":"Minimizing Polarization and Disagreement in Social Networks","arxiv_id":"1712.09948","date":"2017-12-28","proceeding":null,"authors":["Cameron Musco","Christopher Musco","Charalampos E. Tsourakakis"],"abstract":"The rise of social media and online social networks has been a disruptive\nforce in society. Opinions are increasingly shaped by interactions on online\nsocial media, and social phenomena including disagreement and polarization are\nnow tightly woven into everyday life. In this work we initiate the study of the\nfollowing question: given $n$ agents, each with its own initial opinion that\nreflects its core value on a topic, and an opinion dynamics model, what is the\nstructure of a social network that minimizes {\\em polarization} and {\\em\ndisagreement} simultaneously?\n  This question is central to recommender systems: should a recommender system\nprefer a link suggestion between two online users with similar mindsets in\norder to keep disagreement low, or between two users with different opinions in\norder to expose each to the other's viewpoint of the world, and decrease\noverall levels of polarization? Our contributions include a mathematical\nformalization of this question as an optimization problem and an exact,\ntime-efficient algorithm. We also prove that there always exists a network with\n$O(n/\\epsilon^2)$ edges that is a $(1+\\epsilon)$ approximation to the optimum.\nFor a fixed graph, we additionally show how to optimize our objective function\nover the agents' innate opinions in polynomial time.\n  We perform an empirical study of our proposed methods on synthetic and\nreal-world data that verify their value as mining tools to better understand\nthe trade-off between of disagreement and polarization. We find that there is a\nlot of space to reduce both polarization and disagreement in real-world\nnetworks; for instance, on a Reddit network where users exchange comments on\npolitics, our methods achieve a $\\sim 60\\,000$-fold reduction in polarization\nand disagreement.","url_abs":"http://arxiv.org/abs/1712.09948v1","url_pdf":"http://arxiv.org/pdf/1712.09948v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"minimizing-polarization-and-disagreement-in","repo_url":"https://github.com/tsourolampis/polarization-disagreement","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"minimizing-polarization-and-disagreement-in","repo_url":"https://github.com/mayee107/network-disruption","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"minimizing-polarization-and-disagreement-in","repo_url":"https://github.com/mayeechen/network-disruption","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.09948","atlas_url":"https://app.syntology.ai/?focus=1712.09948","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}