MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation
Abstract: Memes present unique moderation challenges due to their subtle, multimodal interplay of images, text, and social context. Standard systems relying predominantly on explicit textual cues often overlook harmful content camouflaged by irony, symbolism, or cultural references. To address this gap, we introduce MemeSense, an adaptive in-context learning framework that fuses social commonsense reasoning with visually and semantically related reference examples. By encoding crucial task information into a learnable cognitive shift vector, MemeSense effectively balances lexical, visual, and ethical considerations, enabling precise yet context-aware meme intervention. Extensive evaluations on a curated set of implicitly harmful memes demonstrate that MemeSense substantially outperforms strong baselines, paving the way for safer online communities. We will make the dataset and source code publicly available upon acceptance.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: Adaptive In-context Learning, Meme Analysis, Multi-modality, NLP
Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources
Languages Studied: English
Submission Number: 6755
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