[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-133723-en":3,"doc-seo-133723-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},133723,2336474466712,"Quinn Holloway","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Trigger Warning Assignment - Multi-Label Document Classification","A trigger warning is used to alert people to potentially disturbing content before consumption. The paper frames trigger warning assignment as a multi-label document classification task, introducing the Webis Trigger Warning Corpus 2022 and the first dataset of 1 million fanfiction works from Archive of our Own with up to 36 warnings per document. It builds a comprehensive taxonomy by mapping 41 million author-provided free-form tags to institutionally recommended warnings, then evaluates state-of-the-art multi-label models and discusses trade-offs across granularity, openness, length, and label confidence.","Trigger Warning Assignment as a Multi-Label Document Classification Problem  \nMatti Wiegmann1 Magdalena Wolska1 Christoper Schröder2 Ole Borchardt2 Benno Stein1 Martin Potthast2 ,3  \n1 Bauhaus-Universität Weimar 2 Leipzig University 3 ScaDS.AI  \nAbstract  \nA trigger warning is used to warn people about potentially disturbing content. We introduce trigger warning assignment as a multi-label classification task, create the Webis Trigger Warning Corpus 2022, and with it the first dataset of 1 million fanfiction works from Archive of our Own with up to 36 different warnings per document. To provide a reliable catalog of trigger warnings, we organized  \n41 million of free-form tags assigned by fanfiction authors into the first comprehensive taxonomy of trigger warnings by mapping them to the 36 institutionally recommended warnings. To determine the best operationalization of trigger warnings, we explore state-of-the-art multi-label models, examining the trade-off between assigning coarse-and fine-grained warnings, open-and closed-set classification, document length, and label confidence. Our models achieve micro-F 1 scores of about 0 .5, which reveals the difficulty of the task. Tailored representations, long input sequences, and a higher recall on rare warnings would help.1 , 2  \n1 Introduction  \nMedia of any kind can address topics and situations that trigger discomfort or stress in some people. To help these people decide in advance whether they want to consume such media, so-called content warnings or trigger warnings can be added to them. Trigger warnings were originally used to help patients with post-traumatic stress disorder. But after being picked up by various internet communities to also warn people tending to be “emotionally triggered” by a topic (e.g., to cry), the set of known trauma triggers has grown to include many more, such as abuse, aggression, discrimination, eating disorders, hate, pornography, or suicide. Today, the two terms are often used interchangeably, with“trigger” referring to the semantic cause.  \n1Code: [https://github.com/webis-de/ACL-23](https://github.com/webis-de/ACL-23)[ ](https://github.com/webis-de/ACL-23)2Data: [https://doi.org/10.5281/zenodo.7976807](https://doi.org/10.5281/zenodo.7976807)  \nFigure 1: Taxonomy of trigger warnings. The three outer rings are alternative groupings of the inner trigger categories. The inner white ring groups 29 triggers into 7 coarse categories, the inner colored ring by relation between actor, subject, and intent, and the outer colored ring by the nature of the harm. The center represents the long tail of the rare triggers, which can be omitted for closed-set classification.  \nFiction in particular can make its readers susceptible to triggers. Many readers “lose themselves”in fictional works, identify with their protagonists, and experience their fate with particular intensity. This may partly explain why the community of the fanfiction website Archive of our Own (AO3)3 isone of the few where trigger warnings are used proactively and as a matter of course: About 50% of the 7.8 million AO3 works have author-assigned warnings. The other half, however, do not, and neither the AO3 moderators nor the readership seem willing or able to fill that gap.  \n3[https://archiveofourown.org](https://archiveofourown.org), where fans write and share stories based on existing characters and worlds from popular media, such as books, movies, or video games (“fanfiction”) .  \n12113  \nProceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 12113–12134 July 9-14, 2023 ©2023 Association for Computational Linguistics  \nIn this paper, we introduce the task of trigger warning assignment as multi-label document classification (MLC) . Our first contribution is the Webis Trigger Warning Corpus 2022 (Webis-Trigger-22) of 8 million fanfiction works (with 58 billion words and 53 million author-assigned free-form tags; Section 3) . Ou","cbCaiu9ubrkAh9Ji","https://ap.wps.com/l/cbCaiu9ubrkAh9Ji","pdf",2453740,2,1,22,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses automatic trigger warning assignment as a multi-label document classification problem for fanfiction texts.\"},{\"question\":\"What dataset and corpus are introduced?\",\"answer\":\"It introduces the Webis Trigger Warning Corpus 2022 (Webis-Trigger-22) and a derived dataset of 1 million fanfiction documents with many taxonomy-based warning labels.\"},{\"question\":\"How is the trigger warning taxonomy constructed?\",\"answer\":\"The authors organize a taxonomy of 36 warnings by mapping large amounts of free-form author tags to the 36 institutionally recommended warning categories.\"}]","Trigger Warning Assignment - Multi-Label Document Classification | PDF",1787225725,55,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"trigger-warning-assignment-multi-label-document-classification","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/trigger-warning-assignment-multi-label-document-classification/133723/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-20",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address?","Question",{"text":76,"@type":77},"The paper addresses automatic trigger warning assignment as a multi-label document classification problem for fanfiction texts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and corpus are introduced?",{"text":81,"@type":77},"It introduces the Webis Trigger Warning Corpus 2022 (Webis-Trigger-22) and a derived dataset of 1 million fanfiction documents with many taxonomy-based warning labels.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the trigger warning taxonomy constructed?",{"text":85,"@type":77},"The authors organize a taxonomy of 36 warnings by mapping large amounts of free-form author tags to the 36 institutionally recommended warning categories.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]