RETHINKING MISINFORMATION DETECTION

A CLAIM SPACE APPROACH TO POLICY FRAMING

Alfie Chadwick

2026-09-23

WHY DO WE WANT TO DETECT MISINFORMATION?

Basic Outline of supervised Machine Learning

Basic Outline of supervised Machine Learning

ISSUES WITH LABELLING MISINFORMATION


Falsehoods aren’t all equally bad

The truth is sometimes deceiving

The future is uncertain

Power is unaccounted for

ISSUES WITH LABELLING MISINFORMATION


Falsehoods aren’t all equally bad

The truth is sometimes deceiving

The future is uncertain

Power is unaccounted for

THE CURRENT STATE OF COMPUTATIONAL FRAMING

“To frame is to select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described.”

“To frame is to select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described.”

CLAIMS

Issue-Signalling Claims: “We care about X”

Impact Claims: “Policy Y impacts issue X in this way”

Evaluation Claims: “We should use policy Y”

CLAIM SPACE ANALYSIS

Compatibility

Issue Rejection

Contradiction

Parallel Frames

Why a new misinformation system?

It doesn’t ask an algorithm to decide what is true.

It maps the structure of disagreement so humans can judge it openly.