2026 International Conference on Networks Computers and Communications (ICNCCom 2026)

Most multimodal fake news detection research assumes that more training images lead to better performance. We test this assumption directly. Using a Light Cross-Modal Attention model that fuses frozen DeBERTa-v3-base and CLIP ViT-B/32 embeddings through a 247K-parameter attention module, we compare detection accuracy across three image collection strategies: generic web scraping via Bing (679 images), curated article-specific images from FakeNewsNet (60 images), and manually collected images from 15 fact-checking organizations (150 images). In a size-controlled comparison (150 vs. 150), curated images outperform generic ones by 14.7 percentage points (93.3% vs. 78.6%, 5-fold CV). The 150 curated images also beat all 679 generic images by 4.5 points. A second experiment on mixed-source data reveals that multimodal fusion degrades by only 1.4% when data sources are combined, while text-only and image-only models drop by 19.6% and 13.5% respectively. These results point to image-text relevance as a more important factor than dataset size in multimodal fake news detection.

fake news detection;multimodal learning;cross-modal attention;image quality;image curation;CLIP;DeBERTa