{"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/ranking-news-feed-updates-on-social-media-a","title":"Ranking news feed updates on social media: A comparative study of supervised models","arxiv_id":null,"date":"2020-01-01","proceeding":"Conference on Knowledge Extraction and Management 2020 1","authors":["Sami Belkacem","Kamel Boukhalfa","Omar Boussaid"],"abstract":"Social media users are overwhelmed by a large number of updates displayed chronologically in their news feed. Moreover, most updates are irrelevant. Ranking news feed updates by relevance has been proposed to help users catch up with the content they may find interesting. For this matter, supervised learning models have been commonly used to predict relevance. However, no comparative study was made to determine the most suitable models. In this work, we select, analyze, and compare six supervised learning algorithms applied to this case study. Experimental results on Twitter highlight that ensemble learning models are the most appropriate to predict the relevance of updates.","url_abs":"https://www.researchgate.net/publication/339043426_Ranking_news_feed_updates_on_social_media_A_comparative_study_of_supervised_models","url_pdf":"https://www.researchgate.net/publication/339043426_Ranking_news_feed_updates_on_social_media_A_comparative_study_of_supervised_models","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":"ranking-news-feed-updates-on-social-media-a","repo_url":"https://github.com/SamBelkacem/Ranking-social-media-news-feed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"news-recommendation","task_name":"News Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"social-media-popularity-prediction","task_name":"Social Media Popularity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}