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This position paper argues that machine learning and natural language processing are a natural fit for a still-emerging research area. These methods can reduce harms from reliance on human review while also challenging core NLP problems such as temporal distribution shifts and multimodal alignment.","Edinburgh Research Explorer  \nPathways to radicalisation  \nOn research for online radicalisation in natural language processing and machine learning  \nCitation for published version:  \nTalat, Z, Schlichtkrull, MS, Madhyastha, P & De Kock, C 2025, Pathways to radicalisation: On research for online radicalisation in natural language processing and machine learning. in A Calabrese, C de Kock, DNozza, FM Plaza-del-Arco, Z Talat & F Vargas (eds), Proceedings of the 9th Workshop on Online Abuse and Harms. Association for Computational Linguistics, Kerrville, TX, USA, pp. 276-283, The 9th Workshop on Online Abuse and Harms, Vienna, Austria, 31/07/25 . \u003C[https://aclanthology.org/2025.woah-1.25/](https://aclanthology.org/2025.woah-1.25/)>  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nProceedings of the 9th Workshop on Online Abuse and Harms  \nPublisher Rights Statement:  \nACL materials are Copyright © 1963–2025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercialShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 29. Apr. 2026  \nPathways to Radicalisation: On Radicalisation Research in Natural Language Processing and Machine Learning  \nZeerak Talat  \nUniversity of Edinburgh [z@zeerak.org](z@zeerak.org)  \nPranava Madhyasta  \nThe Alan Turing Institute City, University of London [pmadhyastha@turing.ac.uk](pmadhyastha@turing.ac.uk)  \nMichael Sejr Schlichtkrull  \nQueen Mary University of London [m.schlichtkrull@qmul.ac.uk](m.schlichtkrull@qmul.ac.uk)  \nChristine de Kock  \nUniversity of Melbourne [christine.dekock@unimelb.edu.au](christine.dekock@unimelb.edu.au)  \nAbstract  \nViolent ideologies flourish in online communities that sanction extremist content. Communication in such communities includes a variety of modalities, such as text, memes, videos, and podcasts, which collectively radicalise their consumers. In this position paper, we argue that radicalisation is a nascent area for which machine learning and NLP are particularly apt. On the one hand, these technologies could mitigate the harms of human review of extremist content and stand to validate theories of radicalisation. On the other, such communities present an avenue for addressing key challenges in machine learning and NLP technologies, such as temporal distribution shiftsand multi-modal alignment.  \n1 Introduction  \nInternet-facilitated radicalisation is an urgent modern challenge, with links to both acts of physical violence and intangible social harms. The proliferation of online content that espouses extremist views presents a challenge for scalable content moderation and prevention of radicalization. For NLP methods tobe applied for such purposes, they must take into account the nature of radicalisation and communication in fora where radicalisation occurs. First, language use in radicalised communities is highly dissimilar from standard language use in more sanitised ar","cbCaicMY7bHhgKKL","https://ap.wps.com/l/cbCaicMY7bHhgKKL","pdf",332656,1,9,"English","en",105,"# Introduction\n## Research framing and urgency\n## Challenges for NLP methods\n## Multimodal and longitudinal nature\n## Position paper contributions","[{\"question\":\"Why is online radicalisation considered an urgent research challenge?\",\"answer\":\"It is linked to both physical violence and less tangible social harms, and the growing volume of extremist content makes scalable moderation and prevention difficult.\"},{\"question\":\"What makes language in radicalised communities different from standard internet language?\",\"answer\":\"Radicalised communities use negative rhetoric and focus heavily on discussions about target groups, producing distributions that diverge from cleaner online settings.\"},{\"question\":\"Which NLP and machine-learning challenges does the paper highlight?\",\"answer\":\"It emphasizes temporal distribution shifts, rapid changes in norms and vocabulary, and the need for models that handle multimodal data and alignment across modalities.\"}]","Pathways to Radicalisation - On Research for Online Radicalisation in Natural Language Processing and Machine Learning | PDF",1785731011,23,{"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},"pathways-to-radicalisation-on-research-for-online-radicalisation-in-natural-language-processing-and-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/pathways-to-radicalisation-on-research-for-online-radicalisation-in-natural-language-processing-and-machine-learning/120633/",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","2026-08-03",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},"Why is online radicalisation considered an urgent research challenge?","Question",{"text":75,"@type":76},"It is linked to both physical violence and less tangible social harms, and the growing volume of extremist content makes scalable moderation and prevention difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes language in radicalised communities different from standard internet language?",{"text":80,"@type":76},"Radicalised communities use negative rhetoric and focus heavily on discussions about target groups, producing distributions that diverge from cleaner online settings.",{"name":82,"@type":73,"acceptedAnswer":83},"Which NLP and machine-learning challenges does the paper highlight?",{"text":84,"@type":76},"It emphasizes temporal distribution shifts, rapid changes in norms and vocabulary, and the need for models that handle multimodal data and alignment across modalities.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]