{"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/leveraging-the-crowd-to-detect-and-reduce-the","title":"Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation","arxiv_id":"1711.09918","date":"2017-11-27","proceeding":null,"authors":["Jooyeon Kim","Behzad Tabibian","Alice Oh","Bernhard Schoelkopf","Manuel Gomez-Rodriguez"],"abstract":"Online social networking sites are experimenting with the following\ncrowd-powered procedure to reduce the spread of fake news and misinformation:\nwhenever a user is exposed to a story through her feed, she can flag the story\nas misinformation and, if the story receives enough flags, it is sent to a\ntrusted third party for fact checking. If this party identifies the story as\nmisinformation, it is marked as disputed. However, given the uncertain number\nof exposures, the high cost of fact checking, and the trade-off between flags\nand exposures, the above mentioned procedure requires careful reasoning and\nsmart algorithms which, to the best of our knowledge, do not exist to date.\n  In this paper, we first introduce a flexible representation of the above\nprocedure using the framework of marked temporal point processes. Then, we\ndevelop a scalable online algorithm, Curb, to select which stories to send for\nfact checking and when to do so to efficiently reduce the spread of\nmisinformation with provable guarantees. In doing so, we need to solve a novel\nstochastic optimal control problem for stochastic differential equations with\njumps, which is of independent interest. Experiments on two real-world datasets\ngathered from Twitter and Weibo show that our algorithm may be able to\neffectively reduce the spread of fake news and misinformation.","url_abs":"http://arxiv.org/abs/1711.09918v1","url_pdf":"http://arxiv.org/pdf/1711.09918v1.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":"leveraging-the-crowd-to-detect-and-reduce-the","repo_url":"https://github.com/Networks-Learning/curb","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.09918","atlas_url":"https://app.syntology.ai/?focus=1711.09918","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}