{"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/misleading-metadata-detection-on-youtube","title":"Misleading Metadata Detection on YouTube","arxiv_id":"1901.08759","date":"2019-01-25","proceeding":null,"authors":["Priyank Palod","Ayush Patwari","Sudhanshu Bahety","Saurabh Bagchi","Pawan Goyal"],"abstract":"YouTube is the leading social media platform for sharing videos. As a result,\nit is plagued with misleading content that includes staged videos presented as\nreal footages from an incident, videos with misrepresented context and videos\nwhere audio/video content is morphed. We tackle the problem of detecting such\nmisleading videos as a supervised classification task. We develop UCNet - a\ndeep network to detect fake videos and perform our experiments on two datasets\n- VAVD created by us and publicly available FVC [8]. We achieve a macro\naveraged F-score of 0.82 while training and testing on a 70:30 split of FVC,\nwhile the baseline model scores 0.36. We find that the proposed model\ngeneralizes well when trained on one dataset and tested on the other.","url_abs":"http://arxiv.org/abs/1901.08759v1","url_pdf":"http://arxiv.org/pdf/1901.08759v1.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":"misleading-metadata-detection-on-youtube","repo_url":"https://github.com/ucnet01/UCNet_Implementation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1901.08759","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}