Beyond Disinformation: Strategic Misrepresentation across Content, Actors, Processes, and Covertness

A research team from multiple institutions has published a groundbreaking paper proposing a new framework called 'strategic misrepresentation' to better detect and analyze coordinated information campaigns. The work, led by Arttu Malkamäki and 10 co-authors, argues that current approaches focusing narrowly on 'disinformation' (intentionally false content) miss more sophisticated manipulation techniques. Their framework expands analysis across four observable dimensions: content distortion (what's said), actor distortion (who says it), process distortion (how it spreads), and covertness (how hidden the coordination is).

This multidimensional approach allows detection of campaigns that don't necessarily spread false information but manipulate perception through coordinated behaviors like astroturfing (creating fake grassroots support), brigading (organized harassment), or algorithmic gaming. The researchers conducted an integrative survey of detection techniques across machine learning, network science, and visual analytics, showing how these methods can jointly operationalize their framework. Their work provides a pragmatic foundation for platforms and researchers to detect, classify, and evaluate both legitimate and illegitimate information campaigns in a more systematic way.