{"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/hateminers-detecting-hate-speech-against","title":"Hateminers : Detecting Hate speech against Women","arxiv_id":"1812.06700","date":"2018-12-17","proceeding":null,"authors":["Punyajoy Saha","Binny Mathew","Pawan Goyal","Animesh Mukherjee"],"abstract":"With the online proliferation of hate speech, there is an urgent need for\nsystems that can detect such harmful content. In this paper, We present the\nmachine learning models developed for the Automatic Misogyny Identification\n(AMI) shared task at EVALITA 2018. We generate three types of features:\nSentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet.\nThese features are then concatenated and fed into the machine learning models.\nOur model came First for the English Subtask A and Fifth for the English\nSubtask B. We release our winning model for public use and it's available at\nhttps://github.com/punyajoy/Hateminers-EVALITA.","url_abs":"http://arxiv.org/abs/1812.06700v1","url_pdf":"http://arxiv.org/pdf/1812.06700v1.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":"hateminers-detecting-hate-speech-against","repo_url":"https://github.com/punyajoy/Hateminers-EVALITA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hateminers-detecting-hate-speech-against","repo_url":"https://github.com/hate-alert/HateALERT-EVALITA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"hate-speech-detection","task_name":"Hate Speech Detection"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hate-speech-detection-on-automatic","task":"Hate Speech Detection","dataset":"Automatic Misogynistic Identification","model":"Logistic Regression","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"0.704"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}