Disinformation Detection

Definition

Disinformation Detection examines identifying intentionally deceptive information operations while separating intent from mere inaccuracy within the broader domain of the accuracy, evidential strength, traceability, confidence and correction lifecycle of claims and knowledge.

Why this matters

Disinformation Detection can materially affect autonomy, safety, public trust, market integrity, community cohesion and institutional decisions. Poorly designed interventions can suppress legitimate speech, entrench bias or create false confidence.

Research questions

Under which conditions can identifying intentionally deceptive information operations while separating intent from mere inaccuracy be measured or improved reliably, and how do effects vary by platform, population, context and intervention?

Hypotheses

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve operation-level detection and attribution more than single-score, content-only or opaque automated approaches.

Proposed methods

claim extraction; evidence retrieval; source-quality assessment; fact verification; calibration studies; knowledge-graph validation; correction propagation analysis; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Disinformation Detection

Stakeholders and beneficiaries

journalists; researchers; libraries; educators; public agencies; platforms; publishers; fact-checkers; citizens