{"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/wikipedia-vandal-early-detection-from-user","title":"Wikipedia Vandal Early Detection: from User Behavior to User Embedding","arxiv_id":"1706.00887","date":"2017-06-03","proceeding":null,"authors":["Shuhan Yuan","Panpan Zheng","Xintao Wu","Yang Xiang"],"abstract":"Wikipedia is the largest online encyclopedia that allows anyone to edit\narticles. In this paper, we propose the use of deep learning to detect vandals\nbased on their edit history. In particular, we develop a multi-source\nlong-short term memory network (M-LSTM) to model user behaviors by using a\nvariety of user edit aspects as inputs, including the history of edit reversion\ninformation, edit page titles and categories. With M-LSTM, we can encode each\nuser into a low dimensional real vector, called user embedding. Meanwhile, as a\nsequential model, M-LSTM updates the user embedding each time after the user\ncommits a new edit. Thus, we can predict whether a user is benign or vandal\ndynamically based on the up-to-date user embedding. Furthermore, those user\nembeddings are crucial to discover collaborative vandals.","url_abs":"http://arxiv.org/abs/1706.00887v1","url_pdf":"http://arxiv.org/pdf/1706.00887v1.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":"wikipedia-vandal-early-detection-from-user","repo_url":"https://bitbucket.org/bookcold/vandal_detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}