{"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/simple-open-stance-classification-for-rumour","title":"Simple Open Stance Classification for Rumour Analysis","arxiv_id":"1708.05286","date":"2017-08-17","proceeding":"RANLP 2017 9","authors":["Ahmet Aker","Leon Derczynski","Kalina Bontcheva"],"abstract":"Stance classification determines the attitude, or stance, in a (typically\nshort) text. The task has powerful applications, such as the detection of fake\nnews or the automatic extraction of attitudes toward entities or events in the\nmedia. This paper describes a surprisingly simple and efficient classification\napproach to open stance classification in Twitter, for rumour and veracity\nclassification. The approach profits from a novel set of automatically\nidentifiable problem-specific features, which significantly boost classifier\naccuracy and achieve above state-of-the-art results on recent benchmark\ndatasets. This calls into question the value of using complex sophisticated\nmodels for stance classification without first doing informed feature\nextraction.","url_abs":"http://arxiv.org/abs/1708.05286v2","url_pdf":"http://arxiv.org/pdf/1708.05286v2.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":"simple-open-stance-classification-for-rumour","repo_url":"https://github.com/RonnieGandhi/ML-Fake_News_Stance_Detect","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"simple-open-stance-classification-for-rumour","repo_url":"https://github.com/radpet/fake-news-detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"stance-classification","task_name":"Stance Classification"},{"task_slug":"veracity-classification","task_name":"Veracity Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.05286","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}