{"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/some-like-it-hoax-automated-fake-news","title":"Some Like it Hoax: Automated Fake News Detection in Social Networks","arxiv_id":"1704.07506","date":"2017-04-25","proceeding":null,"authors":["Eugenio Tacchini","Gabriele Ballarin","Marco L. Della Vedova","Stefano Moret","Luca de Alfaro"],"abstract":"In recent years, the reliability of information on the Internet has emerged\nas a crucial issue of modern society. Social network sites (SNSs) have\nrevolutionized the way in which information is spread by allowing users to\nfreely share content. As a consequence, SNSs are also increasingly used as\nvectors for the diffusion of misinformation and hoaxes. The amount of\ndisseminated information and the rapidity of its diffusion make it practically\nimpossible to assess reliability in a timely manner, highlighting the need for\nautomatic hoax detection systems.\n  As a contribution towards this objective, we show that Facebook posts can be\nclassified with high accuracy as hoaxes or non-hoaxes on the basis of the users\nwho \"liked\" them. We present two classification techniques, one based on\nlogistic regression, the other on a novel adaptation of boolean crowdsourcing\nalgorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users,\nwe obtain classification accuracies exceeding 99% even when the training set\ncontains less than 1% of the posts. We further show that our techniques are\nrobust: they work even when we restrict our attention to the users who like\nboth hoax and non-hoax posts. These results suggest that mapping the diffusion\npattern of information can be a useful component of automatic hoax detection\nsystems.","url_abs":"http://arxiv.org/abs/1704.07506v1","url_pdf":"http://arxiv.org/pdf/1704.07506v1.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":"some-like-it-hoax-automated-fake-news","repo_url":"https://github.com/gabll/some-like-it-hoax","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"misinformation","task_name":"Misinformation"}],"methods":[],"datasets_introduced":[{"slug":"some-like-it-hoax","name":"Some Like it Hoax","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.07506","atlas_url":"https://app.syntology.ai/?focus=1704.07506","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}