[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125834-en":3,"doc-seo-125834-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125834,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Exploring Gender Differences in Fatwa through Machine Learning","Examines how men’s and women’s inquiries differ within a religious context, focusing on whether the popularity of fatwa answers can be forecast and which factors drive that popularity. Builds a new Arabic question–answer dataset (40,000 pairs initially, with an expanded dataset described) from online Q&A platforms. Applies advanced Arabic text preprocessing and machine learning to predict the questioner’s gender, estimate answer popularity, and analyze topic associations across genders.","Please cite the Published Version  \nMohamed, Emad and Sarwar, Raheem  (2024) Exploring Gender Differences in Fatwa through Machine Learning. Journal of Cultural Analytics, 9 (3) .  \nDOI: [https://doi.org/10.22148/001c.116368](https://doi.org/10.22148/001c.116368)  \nPublisher: McGill University, Department of Languages, Literatures, and Cultures  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/635193/](https://e-space.mmu.ac.uk/635193/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article published in Journal of Cultural Analytics by McGill University, Department of Languages, Literatures, and Cultures.  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk](openresearch@mmu.ac.uk. Please)[. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nARTICLE  \nExploring Gender Differences in Fatwa through Machine Learning  \nEmad Mohamed1, Raheem Sarwar2  \n1 Nazarbayev University, 2 Manchester Metropolitan University  \nKeywords: fatwa analysis, gender and religion, machine learning, topic modeling, classification, regression  \n[https://doi.org/10.22148/001c.116368](https://doi.org/10.22148/001c.116368)  \nJournal of Cultural Analytics Vol. 9, Issue 3, 2024  \nThis paper focuses on exploring the differences in inquiries made by men and women within a religious context. Additionally, we aim to ascertain whether it’s feasible to forecast the popularity of answers and the factors contributing to their popularity. To achieve this, we compile a new dataset comprising 40,000 question-answer pairs categorized by gender and popularity. These are sourced from online question-and-answer platforms. Our methodology involves  \ncomprehensive experimental analysis, utilizing advanced Arabic text preprocessing alongside machine learning algorithms. We concentrate on two primary objectives: predicting the gender of the questioner and forecasting the popularity of answers. Furthermore, we delve into thematic variations based on gender and address pivotal research queries that offer new perspectives within this domain. These include investigating the differences between questions posed by women versus men, exploring the potential for automated classification of queries by gender, predicting the popularity of fatwas, and identifying the contributing factors to their popularity. Our experimental findings demonstrate a 98% accuracy in gender prediction, precise predictions of popularity with minimal margin for error, and the identification of topics and their associations that are more inclined towards either men or women. We intend to share both the dataset and the source code openly with the research community.  \n1. Background and Introduction  \nResearch on Muslim women has recently grown rapidly (Faiz et al.; Khan and Mollah; Kloos and Ismah) . This may be due to shifting attention after women in the industrialized world gained considerable rights (Maftuhin; READ and BARTKOWSKI; Nikjoo et al.; Baboolal; Murrar et al.; Abu-Rasand Itzhaki-Braun) . Most prominent among the issues of Muslim women are how (and why) Muslim women wear the hijab (Abu-Lughod; Acker; Brenner) and Muslim women’s political participation (Finlay and Hopkins; Akbarzadeh and Roose; Bhimji) . A recent study of Islam in the English Wikipedia has found that the tenth most salient collocate of the adjectives Muslim/Islamic is the noun woman, ahead of such common collocates as conquest, jurisprudence, state, art, philosophy, terrorism, fundamentalism, and even prophet (Mohamed), but it remains true that “studies on Muslim women’s online activities remain few and","cbCaipCjpGNWGGro","https://ap.wps.com/l/cbCaipCjpGNWGGro","pdf",1318858,1,24,"English","en",105,"# Background and Introduction\n## Research motivation and prior work\n## Dataset construction and data scope\n## Methodology: preprocessing and machine learning\n## Objectives and research questions\n## Experimental results and contributions","[{\"question\":\"What problem does the paper address about gender and fatwa?\",\"answer\":\"It investigates differences in men’s versus women’s religious inquiries and whether the popularity of answers can be predicted.\"},{\"question\":\"How is the dataset for the study constructed?\",\"answer\":\"It compiles Arabic question–answer pairs from online question-and-answer platforms, categorized by gender and popularity.\"},{\"question\":\"Which machine learning tasks does the study perform?\",\"answer\":\"The work predicts the gender of the questioner and forecasts the popularity of answers, while also analyzing gender-linked topic variations.\"}]","Exploring Gender Differences in Fatwa through Machine Learning | PDF",1785901471,60,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploring-gender-differences-in-fatwa-through-machine-learning","",{"@graph":36,"@context":85},[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/exploring-gender-differences-in-fatwa-through-machine-learning/125834/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address about gender and fatwa?","Question",{"text":75,"@type":76},"It investigates differences in men’s versus women’s religious inquiries and whether the popularity of answers can be predicted.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset for the study constructed?",{"text":80,"@type":76},"It compiles Arabic question–answer pairs from online question-and-answer platforms, categorized by gender and popularity.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning tasks does the study perform?",{"text":84,"@type":76},"The work predicts the gender of the questioner and forecasts the popularity of answers, while also analyzing gender-linked topic variations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]