Content authenticity signals: a field guide for trust teams
A practical reference covering the most reliable signals that distinguish authentic content from coordinated manufactured narratives.
Practical research on coordinated inauthentic behaviour, manipulation detection, and building robust brand safety and newsroom trust practices.
A practical reference covering the most reliable signals that distinguish authentic content from coordinated manufactured narratives.
The volume and velocity of coordinated campaigns has outpaced what any team can monitor manually. Here is what the math actually looks like.
Social platforms set their own rules on inauthentic behaviour, but enforcement gaps leave brand safety teams exposed when campaigns target them.
An analysis of the structural and behavioural patterns we observed across 500 internally flagged stories, covering six months of detection data.
High recall without precision is noise, not safety. How detection tools should balance sensitivity against the cost of wrongly flagging legitimate content.
What roles matter most in the first year, which processes to prioritise, and where tooling fills the gap when headcount is limited.
Brands are increasingly the collateral damage of competitor-targeted or politically motivated astroturfing. What the campaigns look like and how to spot them early.
A score that arrives after a story has spread for six hours has limited operational value. The design trade-offs behind fast detection.
Large platforms built significant internal capabilities for detecting coordinated inauthentic behaviour. The methods translate to newsroom contexts more directly than most editorial teams realise.
AI-generated content at volume changes what brand safety teams need to monitor and how detection tools need to adapt.
The propagation mechanics of coordinated manipulation differ from organic viral spread in measurable ways. This is what the patterns look like at the network level.