{"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/detecting-offensive-language-in-tweets-using","title":"Detecting Offensive Language in Tweets Using Deep Learning","arxiv_id":"1801.04433","date":"2018-01-13","proceeding":null,"authors":["Georgios K. Pitsilis","Heri Ramampiaro","Helge Langseth"],"abstract":"This paper addresses the important problem of discerning hateful content in\nsocial media. We propose a detection scheme that is an ensemble of Recurrent\nNeural Network (RNN) classifiers, and it incorporates various features\nassociated with user-related information, such as the users' tendency towards\nracism or sexism. These data are fed as input to the above classifiers along\nwith the word frequency vectors derived from the textual content. Our approach\nhas been evaluated on a publicly available corpus of 16k tweets, and the\nresults demonstrate its effectiveness in comparison to existing state of the\nart solutions. More specifically, our scheme can successfully distinguish\nracism and sexism messages from normal text, and achieve higher classification\nquality than current state-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1801.04433v1","url_pdf":"http://arxiv.org/pdf/1801.04433v1.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":"detecting-offensive-language-in-tweets-using","repo_url":"https://github.com/gpitsilis/hate-speech","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"16k","task_name":"16k"},{"task_slug":"abuse-detection","task_name":"Abuse Detection"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1801.04433","atlas_url":"https://app.syntology.ai/?focus=1801.04433","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}