{"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/comparative-studies-of-detecting-abusive","title":"Comparative Studies of Detecting Abusive Language on Twitter","arxiv_id":"1808.10245","date":"2018-08-30","proceeding":"WS 2018 10","authors":["Younghun Lee","Seunghyun Yoon","Kyomin Jung"],"abstract":"The context-dependent nature of online aggression makes annotating large\ncollections of data extremely difficult. Previously studied datasets in abusive\nlanguage detection have been insufficient in size to efficiently train deep\nlearning models. Recently, Hate and Abusive Speech on Twitter, a dataset much\ngreater in size and reliability, has been released. However, this dataset has\nnot been comprehensively studied to its potential. In this paper, we conduct\nthe first comparative study of various learning models on Hate and Abusive\nSpeech on Twitter, and discuss the possibility of using additional features and\ncontext data for improvements. Experimental results show that bidirectional GRU\nnetworks trained on word-level features, with Latent Topic Clustering modules,\nis the most accurate model scoring 0.805 F1.","url_abs":"http://arxiv.org/abs/1808.10245v1","url_pdf":"http://arxiv.org/pdf/1808.10245v1.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":"comparative-studies-of-detecting-abusive","repo_url":"https://github.com/younggns/comparative-abusive-lang","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"comparative-studies-of-detecting-abusive","repo_url":"https://github.com/JackonYang/maya","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"comparative-studies-of-detecting-abusive","repo_url":"https://github.com/UW-MSDS-DATA-598-Reproducibility-WI20/goel-modi-moroney-ramprasad-replication-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"comparative-studies-of-detecting-abusive","repo_url":"https://github.com/abolim/Reproducibility-Research-Replication","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"abuse-detection","task_name":"Abuse Detection"},{"task_slug":"abusive-language","task_name":"Abusive Language"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"hate-speech-detection","task_name":"Hate Speech Detection"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"twitter-sentiment-analysis","task_name":"Twitter Sentiment Analysis"}],"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}