{"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/satirical-news-detection-and-analysis-using","title":"Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features","arxiv_id":"1709.01189","date":"2017-09-04","proceeding":"EMNLP 2017 9","authors":["Fan Yang","Arjun Mukherjee","Eduard Dragut"],"abstract":"Satirical news is considered to be entertainment, but it is potentially\ndeceptive and harmful. Despite the embedded genre in the article, not everyone\ncan recognize the satirical cues and therefore believe the news as true news.\nWe observe that satirical cues are often reflected in certain paragraphs rather\nthan the whole document. Existing works only consider document-level features\nto detect the satire, which could be limited. We consider paragraph-level\nlinguistic features to unveil the satire by incorporating neural network and\nattention mechanism. We investigate the difference between paragraph-level\nfeatures and document-level features, and analyze them on a large satirical\nnews dataset. The evaluation shows that the proposed model detects satirical\nnews effectively and reveals what features are important at which level.","url_abs":"http://arxiv.org/abs/1709.01189v1","url_pdf":"http://arxiv.org/pdf/1709.01189v1.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":"satirical-news-detection-and-analysis-using","repo_url":"https://github.com/fYYw/satire","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.01189","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}