[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126901-en":3,"doc-seo-126901-105":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},126901,2336474466712,"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 infers author traits from linguistic markers, paralleling dialect classification that predicts linguistic variety from text. Despite progress in variety classification, forensic linguistics often avoids these methods due to limited transparency. This work examines explainable machine learning for a geolinguistic profiling scenario using German-speaking social media data. It identifies the lexical items most influential for variety classification and finds them representative, while the trained models also exploit place names for categorization.","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. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 05. 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","cbCaiisFtr9zeRMK","https://ap.wps.com/l/cbCaiisFtr9zeRMK","pdf",501254,1,"English","en",105,"# Abstract\n# Introduction\n# Related work","[{\"question\":\"What problem does the paper address in forensic linguistics?\",\"answer\":\"The paper addresses the challenge that forensic authorship profiling often uses manual, less transparent methods because common machine learning approaches are treated as black boxes in legal contexts.\"},{\"question\":\"How is explainability used in the study?\",\"answer\":\"Explainability is achieved by identifying which lexical items most strongly influence variety classification, supporting interpretability in a geolinguistic authorship profiling setting.\"},{\"question\":\"What data and task are used for geolinguistic profiling?\",\"answer\":\"The study focuses on variety classification as geolinguistic profiling, using social media data from the German-speaking area to classify the linguistic variety of unknown texts.\"}]","Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling | 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problem does the paper address in forensic linguistics?","Question",{"text":74,"@type":75},"The paper addresses the challenge that forensic authorship profiling often uses manual, less transparent methods because common machine learning approaches are treated as black boxes in legal contexts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is explainability used in the study?",{"text":79,"@type":75},"Explainability is achieved by identifying which lexical items most strongly influence variety classification, supporting interpretability in a geolinguistic authorship profiling setting.",{"name":81,"@type":72,"acceptedAnswer":82},"What data and task are used for geolinguistic profiling?",{"text":83,"@type":75},"The study focuses on variety classification as geolinguistic profiling, using social media data from the German-speaking area to classify the linguistic variety of unknown 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