[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127403-en":3,"doc-seo-127403-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127403,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Explainability of machine learning approaches in forensic linguistics - a case study in geolinguistic authorship profiling","Forensic authorship profiling infers characteristics of an author using linguistic markers, a goal closely related to dialect variety classification where the model predicts a text’s linguistic variety. While recent advances improve variety classification, forensic linguistics often avoids these approaches due to limited transparency. This paper investigates explainability in a forensic setting by using variety classification for geolinguistic profiling of unknown texts from German-speaking social media, identifying the most impactful lexical items and examining model reliance on place names.","University of Birmingham  \nExplainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling  \nRoemling, Dana; Scherrer, Yves; Miletić, Aleksandra  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nRoemling, D , Scherrer, Y & Miletić, A 2024, Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling. in The first international conference on Natural Language Processing and Artificial Intelligence for Cyber Security, NLPAICS’2024: Proceedings. Lancaster University, pp. 10-16, The First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security, Lancaster, United Kingdom, 29/07/24 . \u003C[https://nlpaics.com/conference-proceedings/](https://nlpaics.com/conference-proceedings/)>  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \nExplainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling  \nDana Roemling  \nUniversity of Birmingham University of Helsinki  \n[d.roemling@bham.ac.uk](d.roemling@bham.ac.uk)  \nYves Scherrer  \nUniversity of Oslo  \nUniversity of Helsinki [yves.scherrer@ifi.uio.no](yves.scherrer@ifi.uio.no)  \nAleksandra Mileti  \nUniversity of Helsinki University Sorbonne Nouvelle  \n[aleksandra.miletic@helsinki.fi](aleksandra.miletic@helsinki.fi)  \nAbstract  \nForensic authorship profiling uses linguistic markers to infer characteristics about an author of a text. This task is paralleled in dialect classification, where a prediction is made about the linguistic variety of a text based on the text itself. While there have been significant advances in recent years in variety classification, forensic linguistics rarely relies on these approaches due to their lack of transparency, among other reasons. In this paper we therefore explore the explainability of machine learning approaches considering the forensic context. We focus on variety classificationas a means of geolinguistic profiling of unknown texts based on social media data from the German-speaking area. For this, we identify the lexical items that are the most impactful for the variety classification. We find that the extracted lexical features are indeed representative of their respective varieties and note that the trained models also rely on place names forclassifications.  \n1 Introduction  \nForensic authorship analysis is a key area of rese","cbCaieT7NjDqC3NF","https://ap.wps.com/l/cbCaieT7NjDqC3NF","pdf",501254,1,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Related work","[{\"question\":\"What does forensic authorship profiling aim to infer?\",\"answer\":\"It uses linguistic markers to infer characteristics about an author of a text.\"},{\"question\":\"Why are explainable machine learning approaches important for forensic linguistics?\",\"answer\":\"Forensic use often requires transparency, and current NLP models are frequently criticized as “black-box,” which limits legal applicability.\"},{\"question\":\"How does the paper perform geolinguistic authorship profiling?\",\"answer\":\"It focuses on variety classification to profile the geographic and linguistic variety of unknown texts, using German-speaking social media data and analyzing which lexical items drive predictions.\"}]","Explainability of machine learning approaches in forensic linguistics - a case study in geolinguistic authorship profiling | PDF",1785938700,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"explainability-of-machine-learning-approaches-in-forensic-linguistics-a-case-study-in-geolinguistic-authorship-profiling","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/explainability-of-machine-learning-approaches-in-forensic-linguistics-a-case-study-in-geolinguistic-authorship-profiling/127403/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does forensic authorship profiling aim to infer?","Question",{"text":74,"@type":75},"It uses linguistic markers to infer characteristics about an author of a text.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why are explainable machine learning approaches important for forensic linguistics?",{"text":79,"@type":75},"Forensic use often requires transparency, and current NLP models are frequently criticized as “black-box,” which limits legal applicability.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the paper perform geolinguistic authorship profiling?",{"text":83,"@type":75},"It focuses on variety classification to profile the geographic and linguistic variety of unknown texts, using German-speaking social media data and analyzing which lexical items drive predictions.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]