[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118297-en":3,"doc-seo-118297-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118297,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Author Gender Identification Considering Gender Bias - Conference Paper","Writing style and word choice differ between men and women, both in what the text discusses and in who writes it. This paper focuses on author gender prediction by identifying the gender of the text author. It compares closed- and open-vocabulary approaches across traditional eBook writing and user-generated digital content such as tweets and blogs, and evaluates how supervised machine learning may inherit gender bias from training data. Results show open-vocabulary methods achieve higher prediction performance with less gender bias.","Technological University Dublin  \nARROW@TU Dublin  \n\n| Conference papers | Directorate of Academic Affairs |\n| --- | --- |\n| 2023-02-23\u003Cbr>Author Gender Identification Considering Gender Bias\u003Cbr>Manuela N. Jeyaraj\u003Cbr>Technological University Dublin, d21[124384@mytudublin.ie](124384@mytudublin.ie)\u003Cbr>Sarah Jane Delany\u003Cbr>Technological University Dublin\u003Cbr>Follow this and additional works at: [https://arrow.tudublin.ie/diraacon](https://arrow.tudublin.ie/diraacon)\u003Cbr> Part of the Other Computer Engineering Commons |  |\n\nRecommended Citation  \nJeyaraj, Manuela N. and Delany, Sarah Jane, \"Author Gender Identification Considering Gender Bias\"(2023) . Conference papers. 25.  \n[https://arrow.tudublin.ie/diraacon/25](https://arrow.tudublin.ie/diraacon/25)  \nThis Conference Paper is brought to you for free and open access by the Directorate of Academic Affairs at ARROW@TU Dublin. It has been accepted for inclusion in Conference papers by an authorized administrator of ARROW@TU Dublin. For more information, please contact [arrow.admin@tudublin.ie](arrow.admin@tudublin.ie), [aisling.coyne@tudublin.ie](aisling.coyne@tudublin.ie),  \n[vera.ki](vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Funder: Science Foundation Ireland  \nAuthor Gender Identification Considering Gender Bias  \nManuela Nayantara Jeyaraj(B) and Sarah Jane Delany  \nTechnological University Dublin, Dublin, Ireland [manuela.n.jeyaraj@mytudublin.ie](manuela.n.jeyaraj@mytudublin.ie), [sarahjane.delany@tudublin.ie](sarahjane.delany@tudublin.ie)  \nAbstract. Writing style and choice of words used in textual content can vary between men and women both in terms of who the text is talking about and whois writing the text. The focus of this paper is on author gender prediction, identifying the gender of who is writing the text. We compare closed and open vocabulary approaches on different types of textual content including more traditional writing styles such as in books, and more recent writing styles used in user generated content on digital platforms such as blogs and social media messaging. As supervised machine learning approaches can reflect human biases in the data they are trained on, we also consider the gender bias of the different approaches across the different types of dataset. We show that open vocabulary approaches perform better both in terms of prediction performance and with less gender bias.  \nKeywords: Author gender identification · Gender bias · Open-vocabulary  \napproach  \n1 Introduction  \nDuring the 2017 Labor leadership election in Britain, an analysis of the language used in news articles about the candidates showed discrepancies related to their gender in how they were described 1. The single male candidate was more likely to be discussed in terms of professional employment, politics and law and order and the two female candidates were much more likely to be discussed in terms of their families, in particular their fathers.  \nThe language style, choice of words, etc. in text differs between men and women [3] . This can be viewed from 2 perspectives; one is towards the subject of the text (inferring whether the person discussed in the text is male or female), and the other is towards the author of the text (inferring whether the author of that text is male or female based on their style of writing) . Our focus in this paper is on author gender identification which is the latter case.  \nPrevious research in supervised learning for author gender prediction has generally used a closed vocabulary approach [9, 36] . The vocabulary used to represent the text is typically a list of characteristics of the text structure and content such as character frequencies and word or sentence count, vocabulary richness measures and the frequencies  \n1 Gender bias in Political description of candidates: [https://www.theguardian.com/technology/](https://www.theguardian.co","cbCaiuoU2cVJWbKb","https://ap.wps.com/l/cbCaiuoU2cVJWbKb","pdf",501210,1,13,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction\n## Gender differences in language use\n## Closed vs open vocabulary approaches\n## Datasets: user-generated vs traditional texts\n## Gender bias in prediction models\n## Hybrid POS feature set","[{\"question\":\"What do the results suggest about gender bias?\",\"answer\":\"Open-vocabulary approaches show significantly less gender bias than closed approaches across all evaluated datasets, while also improving prediction performance.\"}]","Author Gender Identification Considering Gender Bias - Conference Paper | PDF",1785682887,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"author-gender-identification-considering-gender-bias-conference-paper","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/author-gender-identification-considering-gender-bias-conference-paper/118297/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What do the results suggest about gender bias?","Question",{"text":75,"@type":76},"Open-vocabulary approaches show significantly less gender bias than closed approaches across all evaluated datasets, while also improving prediction performance.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]