When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911301873172480 |
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| author | Chauhan, Saumya Hong, Mila Vazhaeparambil, Maria |
| author_facet | Chauhan, Saumya Hong, Mila Vazhaeparambil, Maria |
| contents | AI-generated content and misinformation are increasingly prevalent on social networks. While prior research primarily examined textual misinformation, fewer studies have focused on visual content's role in virality. In this work, we present the first large-scale analysis of how misinformation and AI-generated images propagate through repost cascades across five ideologically diverse Reddit communities. By integrating textual sentiment, visual attributes, and diffusion metrics (e.g., time-to-first repost, community reach), our framework accurately predicts both immediate post-level virality (AUC=0.83) and long-term cascade-level spread (AUC=0.998). These findings offer essential insights for moderating synthetic and misleading visual content online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit Chauhan, Saumya Hong, Mila Vazhaeparambil, Maria Social and Information Networks Artificial Intelligence AI-generated content and misinformation are increasingly prevalent on social networks. While prior research primarily examined textual misinformation, fewer studies have focused on visual content's role in virality. In this work, we present the first large-scale analysis of how misinformation and AI-generated images propagate through repost cascades across five ideologically diverse Reddit communities. By integrating textual sentiment, visual attributes, and diffusion metrics (e.g., time-to-first repost, community reach), our framework accurately predicts both immediate post-level virality (AUC=0.83) and long-term cascade-level spread (AUC=0.998). These findings offer essential insights for moderating synthetic and misleading visual content online. |
| title | When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2512.04639 |