A new arXiv paper presents a method for identifying ragebait — content deliberately crafted to provoke outrage and boost engagement — at scale on the Japanese X platform. The authors move beyond binary detection toward characterizing how such posts are written and spread across a large sample. The work aims to fill gaps in reliable, large-scale measurement of ragebait online.
Researchers propose SyRHM, a method for detecting harmful memes without task-specific training data. It targets implicit harm that comes from mismatches between image and text or from cultural stereotypes, which tripped up earlier multimodal detectors. The approach adds symbolic-language reasoning alongside associative retrieval to improve zero-shot performance.
A new arXiv paper investigates whether foundation models can apply complex content moderation policies reliably and consistently. The study compares two ways of translating moderation rules into model behavior: conveying them through explicit instructions versus through illustrative examples. The findings are relevant to platforms seeking scalable, automated moderation of online content.
A new arXiv paper examines Bluesky's content moderation system by analyzing its publicly accessible moderation logs, which most major platforms keep hidden. The authors characterize how much of the work is automated versus human-reviewed and catalog the types of harms that trigger moderation actions. The work argues that decentralized, transparent logging enables empirical moderation research that opaque platforms have long prevented.
Researchers introduce SIRF, a foundation model designed for content risk control in industrial settings. The work argues that deployment success depends less on average accuracy and more on how much risky content can be automatically handled while maintaining high precision and sub-second response times. The model internalizes specification rules rather than relying on external filtering logic.