{"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/stance-classification-for-rumour-analysis-in","title":"Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure","arxiv_id":"1901.01911","date":"2019-01-07","proceeding":null,"authors":["Endang Wahyu Pamungkas","Valerio Basile","Viviana Patti"],"abstract":"Analysing how people react to rumours associated with news in social media is\nan important task to prevent the spreading of misinformation, which is nowadays\nwidely recognized as a dangerous tendency. In social media conversations, users\nshow different stances and attitudes towards rumourous stories. Some users take\na definite stance, supporting or denying the rumour at issue, while others just\ncomment it, or ask for additional evidence related to the veracity of the\nrumour. On this line, a new shared task has been proposed at SemEval-2017 (Task\n8, SubTask A), which is focused on rumour stance classification in English\ntweets. The goal is predicting user stance towards emerging rumours in Twitter,\nin terms of supporting, denying, querying, or commenting the original rumour,\nlooking at the conversation threads originated by the rumour. This paper\ndescribes a new approach to this task, where the use of conversation-based and\naffective-based features, covering different facets of affect, has been\nexplored. Our classification model outperforms the best-performing systems for\nstance classification at SemEval-2017 Task 8, showing the effectiveness of the\nfeature set proposed.","url_abs":"http://arxiv.org/abs/1901.01911v1","url_pdf":"http://arxiv.org/pdf/1901.01911v1.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":"stance-classification-for-rumour-analysis-in","repo_url":"https://github.com/dadangewp/SemEval2017-RumourEval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"rumour-detection","task_name":"Rumour Detection"},{"task_slug":"stance-classification","task_name":"Stance Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}