{"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/can-who-edits-what-predict-edit-survival","title":"Can Who-Edits-What Predict Edit Survival?","arxiv_id":"1801.04159","date":"2018-01-12","proceeding":null,"authors":["Ali Batuhan Yardım","Victor Kristof","Lucas Maystre","Matthias Grossglauser"],"abstract":"As the number of contributors to online peer-production systems grows, it\nbecomes increasingly important to predict whether the edits that users make\nwill eventually be beneficial to the project. Existing solutions either rely on\na user reputation system or consist of a highly specialized predictor that is\ntailored to a specific peer-production system. In this work, we explore a\ndifferent point in the solution space that goes beyond user reputation but does\nnot involve any content-based feature of the edits. We view each edit as a game\nbetween the editor and the component of the project. We posit that the\nprobability that an edit is accepted is a function of the editor's skill, of\nthe difficulty of editing the component and of a user-component interaction\nterm. Our model is broadly applicable, as it only requires observing data about\nwho makes an edit, what the edit affects and whether the edit survives or not.\nWe apply our model on Wikipedia and the Linux kernel, two examples of\nlarge-scale peer-production systems, and we seek to understand whether it can\neffectively predict edit survival: in both cases, we provide a positive answer.\nOur approach significantly outperforms those based solely on user reputation\nand bridges the gap with specialized predictors that use content-based\nfeatures. It is simple to implement, computationally inexpensive, and in\naddition it enables us to discover interesting structure in the data.","url_abs":"http://arxiv.org/abs/1801.04159v2","url_pdf":"http://arxiv.org/pdf/1801.04159v2.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":"can-who-edits-what-predict-edit-survival","repo_url":"https://github.com/lca4/interank","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}