Trust and Safety

Taxonomies do not investigate campaigns by themselves A framework is a very respectable filing cabinet. DISARM, the Disinformation Analysis and Risk Management framework, gives analysts a standardized vocabulary for describing foreign information manipulation and interference, or FIMI. It organizes influence operations into tactics, techniques, and procedures. That is useful. It gives researchers, governments, platform teams, and security practitioners a shared language instead of a pile of screenshots, vibes, and mutually incompatible spreadsheets. ...

Moderation teams do not usually lose because they see nothing. They lose because they see too much: thousands of accounts posting near the same topic, near the same time, with enough similarity to look suspicious and enough difference to remain deniable. Some are campaign assets. Some are enthusiastic humans. Some are bots. Some are people who simply saw the same trending story and behaved like everyone else, which is annoying for both democracy and data science. ...

Moderation teams live inside an annoying counterfactual. A user posts something toxic. The platform sends a warning, hides the post, suspends the account, or does nothing. A week later, the team can measure what happened. What it cannot observe is the parallel platform where the same user, same thread, same sequence of replies, and same ambient mood unfolded without that intervention. ...

TL;DR for operators SEER is not a “sentiment detector for lies.” That would be wonderfully simple and operationally disastrous. It is a multimodal fake-news detection architecture that first tries to make images more semantically usable, then adds emotion as a probabilistic auxiliary signal rather than a moral verdict. The practical workflow is easy to understand: generate a caption for the image, align the text-image relationship using CLIP-style representations, fuse text, image, and caption features through attention, then use an expert emotional reasoning module to learn how emotional tone correlates with authenticity in the dataset. The paper reports accuracy of 0.929 on Weibo and 0.931 on Twitter, outperforming the tested baselines.1 ...