[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273623-105":59,"doc-detail-273623-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","comment-level-topic-drift-analysis-in-the-reddit-corpus","Comment-level Topic Drift Analysis in the Reddit Corpus","","This work introduces an embedding-based dynamic topic modeling method to detect and quantify topic drift at the comment level in a very large Reddit dataset. Contextual semantic embeddings are generated from short text and analyzed using unsupervised clustering across monthly time windows spanning 2006–2022. Cluster alignment enables topic trajectories and interpretable measures of semantic displacement and discourse evolution in embedding space. A null model comparison test filters spurious dynamics and shows that politically and socially contentious topics drift directionally beyond the null model, while music and sports remain comparatively stable.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comment-level-topic-drift-analysis-in-the-reddit-corpus/273623/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comment-level-topic-drift-analysis-in-the-reddit-corpus/273623.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the method measure topic drift at the comment level?","Question",{"text":113,"@type":114},"It embeds Reddit comments into a contextual semantic embedding space, clusters embeddings within monthly windows, aligns similar clusters across time, and treats the resulting movements as topic trajectories.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What dataset scale and time range are used?",{"text":118,"@type":114},"The analysis uses 12.7 billion Reddit comments spanning 2006 to 2022.",{"name":120,"@type":111,"acceptedAnswer":121},"How is meaningful drift distinguished from noise?",{"text":122,"@type":114},"The approach proposes a null model comparison test that separates genuine semantic drift and discourse evolution from spurious dynamics.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},273623,1789472306,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Comment-level Topic Drift Analysis in the Reddit Corpus  \nSteven Morse and Dan Runfola and Trenton W. Ford William & Mary  \n{stmorse, dsmillerrunfol, [twford}](twford}@wm.edu)[@wm.edu](twford}@wm.edu)  \narXiv : 2608 . 19 133v 1 [cs .CL] 19 Aug 2026  \nAbstract  \nWe present a novel application of embeddingbased dynamic topic modeling techniques to detect and quantify topic drift at the comment level in a massive corpus. By leveraging pretrained language models to generate contextualized semantic embeddings for short text, we analyzed 12.7 billion Reddit comments spanning 2006 to 2022 . Using unsupervised methods on these embeddings, we identify dynamically evolving topic clusters over time. Our primary contribution is a methodology for analysis of semantic drift and discourse evolution in the embedding space itself. We also demonstrate modifications to existing methods that enable this analysis at scale, and we propose and demonstrate a null model comparison test to filter spurious dynamics. Key findings suggest that politically and socially contentious topics exhibit significant directional drift in embedding space, with inter-topic distances changing systematically over time beyond what the null model can explain, whereas domains such as music and sports remain comparatively stable.  \n1 Introduction  \nAttention-based language models encode language into a high-dimensional embedding space that captures both semantic relationships and long-range contextual dependencies, enabling a nuanced representation of meaning. This has enabled the study of topic analysis as computation in a vector space (Churchill and Singh, 2022 ; Wu et al., 2024) . Beyond static clustering, embedding-based methods enable quantitative comparisons among words, sentences, and topics (Grootendorst, 2022) . Yet most pipelines remain descriptive: they list topics or track keyword changes, but they do not estimate whether topics move meaningfully through embedding space over time or compare those movements across topics. That gap matters because the contextualized semantics of topic discourse shifts with  \nevents and norms; without a time-resolved geometric model, we cannot measure or forecast that change.  \nWe address this gap by treating change in topical discourse as trajectories in embedding space and asking when observed movement exceeds stochastic variation. Concretely, we embed comments, cluster within monthly windows, align similar clusters across time to recover topic paths, and test those paths against a null model to separate genuine drift from noise. At web-scale over multiple decades, this yields interpretable measures of displacement and direction and exposes inter-topic convergence and divergence—for example, two topics may retain the same representative words (for example, “police” and “racism”) but grow closer in semantic space, implying a change in semantic similarity of content not detectable by methods treating topics as bags of words. The result is a practical framework for tracking discourse change, evaluating interventions, and informing downstream tasks such as retrieval, moderation, and forecasting.  \n1.1 Related work  \nTopic modeling identifies and organizes latent themes in a collection of texts (Vayansky and Kumar, 2020), and it categorizes broadly into approaches based on distributional language frameworks and those leveraging deep neural network embeddings of language (Churchill and Singh, 2022) . Within the distributional paradigm, considerable research has focused on modeling temporal dynamics and, as a natural extension, analyzing changes in topic distributions (“drift”) (Khan and Wakabayashi, 2021 ; Knights et al., 2009 ; Liu et al., 2013) . Within the neural, or algorithmic paradigm, similar extensions exist, with most studies focused at the word (or token) level (Bamler and Mandt, 2017 ; Periti and Montanelli, 2024) . In this section, we review this literature and introduce our own  \nFigure 1: Clustering in embedded space yiel","cbCaiavC3TOBRY8V","https://ap.wps.com/l/cbCaiavC3TOBRY8V","pdf",1810646,15,"English","# Abstract\n# 1 Introduction\n## 1.1 Related work","[{\"question\":\"How does the method measure topic drift at the comment level?\",\"answer\":\"It embeds Reddit comments into a contextual semantic embedding space, clusters embeddings within monthly windows, aligns similar clusters across time, and treats the resulting movements as topic trajectories.\"},{\"question\":\"What dataset scale and time range are used?\",\"answer\":\"The analysis uses 12.7 billion Reddit comments spanning 2006 to 2022.\"},{\"question\":\"How is meaningful drift distinguished from noise?\",\"answer\":\"The approach proposes a null model comparison test that separates genuine semantic drift and discourse evolution from spurious dynamics.\"}]","Comment-level Topic Drift Analysis in the Reddit Corpus | PDF",38]