[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126902-en":3,"doc-seo-126902-105":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},126902,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Explainability of machine learning approaches in forensic linguistics - a case study in geolinguistic authorship profiling","Forensic authorship profiling uses linguistic markers to infer characteristics of an author from a text, paralleling dialect classification where a text’s linguistic variety is predicted from its own content. In forensic linguistics, such models are often avoided due to limited transparency. This paper examines explainability in a forensic setting by using variety classification for geolinguistic profiling of unknown German-speaking authors from social media data, identifying the lexical items most responsible for predictions.","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","cbCaisGWo9CoY5js","https://ap.wps.com/l/cbCaisGWo9CoY5js","pdf",501254,1,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Related work","[{\"question\":\"What problem does the paper address in forensic authorship profiling?\",\"answer\":\"It addresses the limited transparency of machine-learning approaches used for forensic authorship profiling, which can hinder adoption in legal contexts.\"},{\"question\":\"How is explainability studied in the paper’s geolinguistic authorship profiling case?\",\"answer\":\"The study uses variety classification to infer geolinguistic information from social media text in the German-speaking area and identifies the lexical items most impactful for the model’s predictions.\"},{\"question\":\"What features do the trained models rely on for variety classification?\",\"answer\":\"The paper reports that extracted lexical features are representative of the varieties, and that the models also rely on place names for classification decisions.\"}]","Explainability of machine learning approaches in forensic linguistics - a case study in geolinguistic authorship profiling | PDF",1785935512,20,{"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/126902/",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 problem does the paper address in forensic authorship profiling?","Question",{"text":74,"@type":75},"It addresses the limited transparency of machine-learning approaches used for forensic authorship profiling, which can hinder adoption in legal contexts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is explainability studied in the paper’s geolinguistic authorship profiling case?",{"text":79,"@type":75},"The study uses variety classification to infer geolinguistic information from social media text in the German-speaking area and identifies the lexical items most impactful for the model’s predictions.",{"name":81,"@type":72,"acceptedAnswer":82},"What features do the trained models rely on for variety classification?",{"text":83,"@type":75},"The paper reports that extracted lexical features are representative of the varieties, and that the models also rely on place names for classification decisions.","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"]