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It frames detection at scale while aiming for explainability and robust interpretation using knowledge-driven approaches.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},352428,1791748860,{"code":4,"msg":82,"data":83},"success",[84,87,92,97,102,107,112,117,122],{"id":55,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":85,"slug":86},90,"presentations",{"id":88,"doc_module":22,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},12,"Resumes",80,"resumes",{"id":93,"doc_module":22,"doc_module_name":25,"category_name":94,"show_sort_weight":95,"slug":96},14,"Invoices",70,"invoices",{"id":98,"doc_module":22,"doc_module_name":25,"category_name":99,"show_sort_weight":100,"slug":101},15,"Posters",60,"posters",{"id":103,"doc_module":22,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},16,"Social Media",50,"social-media",{"id":108,"doc_module":22,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},17,"Forms",40,"forms",{"id":113,"doc_module":22,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},18,"Letters",30,"letters",{"id":118,"doc_module":22,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},21,"Paper Templates",5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":82,"data":127},{"doc_id":79,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":135,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":55},687207024478,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","VU Research Portal  \nCapturing the Semantics of Internet Memes  \nIlievski, Filip; Tommasini, Riccardo  \npublished in  \nHandbook on Neurosymbolic AI and Knowledge Graphs 2025  \nDOI (link to publisher)  \n10.3233/FAIA250243  \ndocument version  \nPublisher's PDF, also known as Version of record  \ndocument license  \nArticle 25fa Dutch Copyright Act  \nLink to publication in VU Research Portal  \ncitation for published version (APA)  \nIlievski, F. , & Tommasini, R. (2025) . Capturing the Semantics of Internet Memes. In P. Hitzler, A. Dalal, M. Saeid Mahdavinejad, & S. Saki Norouzi (Eds. ), Handbook on Neurosymbolic AI and Knowledge Graphs (pp. 1057- 1087) . (Frontiers in Artificial Intelligence and Applications; Vol. 400) . IOS Press.  \n[https://doi.org/10.3233/FAIA250243](https://doi.org/10.3233/FAIA250243)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nE-mail address:  \n[vuresearchportal.ub@vu.nl](vuresearchportal.ub@vu.nl)  \n[Download date: 22](Download date: 22) . Sep. 2026  \nCapturing the Semantics of Internet  \nMemes  \nFilip ILIEVSKI a Riccardo TOMMASINIb  \na Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands  \nbINSA Lyon, France  \nAbstract. Internet memes have emerged as a novel format for expressing ideas on the web. They have evolved from a simple form of entertainment to a sophisticated medium of communication that reflects societal sentiments, values, trends, and occasionally unethical and harmful behavior. Internet memes are multimodal, succinct, relatable, and fluid, making them challenging and inspiring for many stakeholders, including content moderators, marketing strategists, and social science researchers. Interpreting internet memes is also a challenging objective for (neurosymbolic) AI, requiring knowledge about memes to be collected, curated, enriched, and integrated with multimodal processing models. This chapter provides background on internet memes and discusses key considerations in machines’ interpretation of memes towards developing robust and explainable methods that can estimate meme similarity, detect toxic memes at scale, and reason about the impact of cultural and personal values. We discuss challenges for meme knowledge collection and curation and describe our approach to generating the Internet Meme Knowledge Graph. We describe methods for estimating meme similarity to ground memes from the web, designed to capture content, form, identity, and stance. Wereview ongoing efforts to develop explainable hate speech detection methods in memes. Finally, we list open challenges and promising future directions for reliable  \nAI reasoning over internet memes.  \nKeywords. internet memes, semantics, visual language models, knowledge graphs  \n1. Introduction  \nInternet memes have emerged as a novel format for expressing ideas on the web, evolving from a simple form of entertainment to a sophisticated medium of communication that reflects societal sentiments, values, and trends, and occasionally unethical and harmful behavior. An internet meme (IM) can be defined as a piece of culture, typically a joke, which gains influence through online transmission [1] . Internet memes are based on a medium, typically an image representing a well-under","cbCaiptfMhun3gmz","https://ap.wps.com/l/cbCaiptfMhun3gmz","pdf",27177703,32,"English","# Abstract\n# 1. Introduction\n## Internet memes as culture and communication\n## Stakeholders and interpretation challenges\n## Motivation for AI and multimodal/neurosymbolic approaches","[{\"question\":\"What makes interpreting internet memes difficult for AI systems?\",\"answer\":\"Internet memes are multimodal, concise, relatable, and fluid, which produces complex semantics. Their interpretation also requires meme-specific knowledge and integration with multimodal processing and knowledge representation.\"},{\"question\":\"What does the chapter propose to address meme interpretation?\",\"answer\":\"It discusses collecting, curating, enriching, and integrating meme knowledge and presents an approach to generating an Internet Meme Knowledge Graph. The chapter also considers methods for estimating meme similarity and reasoning about cultural and personal values.\"},{\"question\":\"How can AI help with toxic meme detection described in the chapter?\",\"answer\":\"The chapter highlights ongoing efforts to develop explainable hate speech detection methods for memes. It frames detection at scale while aiming for explainability and robust interpretation using knowledge-driven approaches.\"}]","Capturing the Semantics of Internet Memes - Chapter 1 - Overview | PDF",1790099549]