{"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/predicting-factuality-of-reporting-and-bias","title":"Predicting Factuality of Reporting and Bias of News Media Sources","arxiv_id":"1810.01765","date":"2018-10-02","proceeding":"EMNLP 2018 10","authors":["Ramy Baly","Georgi Karadzhov","Dimitar Alexandrov","James Glass","Preslav Nakov"],"abstract":"We present a study on predicting the factuality of reporting and bias of news\nmedia. While previous work has focused on studying the veracity of claims or\ndocuments, here we are interested in characterizing entire news media. These\nare under-studied but arguably important research problems, both in their own\nright and as a prior for fact-checking systems. We experiment with a large list\nof news websites and with a rich set of features derived from (i) a sample of\narticles from the target news medium, (ii) its Wikipedia page, (iii) its\nTwitter account, (iv) the structure of its URL, and (v) information about the\nWeb traffic it attracts. The experimental results show sizable performance\ngains over the baselines, and confirm the importance of each feature type.","url_abs":"http://arxiv.org/abs/1810.01765v1","url_pdf":"http://arxiv.org/pdf/1810.01765v1.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":"predicting-factuality-of-reporting-and-bias","repo_url":"https://github.com/ramybaly/News-Media-Reliability","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"predicting-factuality-of-reporting-and-bias","repo_url":"https://github.com/parker-ibm/news-extension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"fact-checking","task_name":"Fact Checking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.01765","atlas_url":"https://app.syntology.ai/?focus=1810.01765","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}