[2603.03315] M-QUEST -- Meme Question-Understanding Evaluation on Semantics and Toxicity

Computer Science > Computation and Language arXiv:2603.03315 (cs) [Submitted on 9 Feb 2026] Title:M-QUEST -- Meme Question-Understanding Evaluation on Semantics and Toxicity Authors:Stefano De Giorgis, Ting-Chih Chen, Filip Ilievski View a PDF of the paper titled M-QUEST -- Meme Question-Understanding Evaluation on Semantics and Toxicity, by Stefano De Giorgis and 2 other authors View PDF HTML (experimental) Abstract:Internet memes are a powerful form of online communication, yet their nature and reliance on commonsense knowledge make toxicity detection challenging. Identifying key features for meme interpretation and understanding, is a crucial task. Previous work has been focused on some elements contributing to the meaning, such as the Textual dimension via OCR, the Visual dimension via object recognition, upper layers of meaning like the Emotional dimension, Toxicity detection via proxy variables, such as hate speech detection, and sentiment analysis. Nevertheless, there is still a lack of an overall architecture able to formally identify elements contributing to the meaning of a meme, and be used in the sense-making process. In this work, we present a semantic framework and a corresponding benchmark for automatic knowledge extraction from memes. First, we identify the necessary dimensions to understand and interpret a meme: Textual material, Visual material, Scene, Background Knowledge, Emotion, Semiotic Projection, Analogical Mapping, Overall Intent, Target Community...