Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 77 references
TL;DR
Results indicate that the proposed ToxiLEN framework improves Chinese misogynistic meme detection and generates CNs that are contextually aligned and less repetitive than the evaluated baselines.
Hateful memes carry harm through the complex combination of image and text, requiring a detector to analyze both modalities jointly. This study investigates whether a short, text-aware semantic description of each meme, generated once by a small vision-language model, adds complementary information beyond direct image-...
Shalini Chawla, Garima Tyagi· International Journal of Sci...· 0 citations
Misogynistic memes often convey harmful gender stereotypes through implicit interactions between images, text, and culturally specific references, making them difficult to detect with unimodal or general-purpose vision-language models. In this paper, we present a Chinese CLIP-based multimodal framework for misogynistic...
Mei-Lin Wu, Qin-Yue Li· Proceedings of the 28th Inte...· 1 citation
Online misogyny increasingly appears in multimodal formats such as memes. Memes combine text and images to convey humor, sarcasm, and ideology. Misogynistic meaning is often implicit and culturally grounded. A meme interpreted as harmful in one cultural setting may be perceived differently in another. Based on the comp...
A novel, comprehensive taxonomy of multimodal misinformation grounded in an in-depth qualitative analysis of the data and prior theoretical work is developed, which leads to previously undocumented insights about how social media users combine images with text to spread misinformation in the wild.
Nadav Borenstein, Greta Warren, Desmond Elliott et al.· 0 citations
This work introduces MemeCULT-1K, a multilingual benchmark of 1,000 South Asian memes in Bengali, English, and Hindi, where each meme is paired with a cultural context note and three human-written explanations, along with a supplementary set of 54 Bengali regional dialect memes.
Tawsif Tashwar Dipto, Mehedi Ahamed, Radib Bin Kabir et al.· 0 citations
The Cross-Cultural Misogynistic Meme Detection Grand Challenge, CC-MMD 2026, addresses the problem of identifying misogynistic content in multimodal memes across culturally diverse annotation perspectives. Existing misogyny detection benchmarks have advanced multimodal content moderation, but most assume a single groun...
Rahul Ponnusamy, Bhuvaneswari Sivagnanam, Anshid K. A. Kizhakkeparambil et al.· Proceedings of the 28th Inte...· 5 citations
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