How Narrative Mutation Tracking Works
When a news story breaks, the initial facts are often straightforward. But as the story spreads across outlets, each source adds its own framing, emphasis, and political spin. Our Narrative Mutation tracker groups articles about the same event and shows you exactly how the story's presentation changes from source to source.
Framing Comparison
We compare how different outlets frame the same event. One source might call a protest "citizens demanding rights" while another calls it "disruption of public order." These framing differences reveal editorial bias that's often invisible when reading just one source.
Drift Detection
Our AI tracks when narratives drift from the original facts. If early coverage is factual but later articles add unverified claims or political spin, we flag that drift — helping you distinguish original reporting from embellished retellings.
Methodology: Narrative Drift Analysis
Narrative mutation tracking measures how a story's framing changes as it propagates across news sources. The system identifies the first published article about an event (the "first mover"), then quantifies how each subsequent article diverges from that baseline in terms of tone and political framing.
Sentiment Drift
Sentiment drift measures how each article's tone differs from the first mover's coverage. A positive drift means the article frames the story more positively than the original; a negative drift means a more negative framing. Sentiment is scored on a 0-100 scale, and drift is calculated as the difference from the baseline. Large sentiment drifts indicate that later coverage is adding emotional spin not present in the original reporting.
Political Drift
Political drift tracks shifts in left-right framing relative to the first mover. Each article is classified on a political spectrum, and the drift value shows how far the framing moved from the original. A story that begins with neutral framing but acquires a partisan angle as it spreads will show increasing political drift values in the timeline. This helps identify when and where political spin enters the news cycle.
Mutation Score
The mutation score is a combined metric incorporating sentiment range and polarisation shift. Stories with high mutation scores changed significantly as they spread across outlets. A high-mutation story might start as a factual report and evolve into a politically charged narrative, or begin neutral and accumulate increasingly emotional language. The score is displayed for each story in the story selector to help you find the most dramatically mutated narratives.
Research basis: Narrative mutation analysis draws on framing theory (Entman, 1993) and studies of news diffusion patterns (Leskovec et al., 2009). The "first mover baseline" approach follows methods from viral misinformation research.
How to Interpret the Results
Reading the Drift Chart
The drift chart plots each article as a data point in publication order, from left to right. The horizontal zero line represents the first mover's framing. The solid blue line traces sentiment drift, showing whether later articles became more positive or more negative. The dashed red line traces political drift, showing whether framing shifted leftward or rightward. A green dot marks the first mover (the baseline article). When both lines diverge sharply from zero, the story underwent significant mutation.
Understanding the Propagation Timeline
The timeline below the chart lists every article about the story in chronological order. Each entry shows the source, publication time, sentiment drift, and political drift values. Articles flagged as "First Mover" are the baseline. Subsequent articles display colour-coded drift badges: red for significant divergence, grey for minimal change. Accountability flags indicate articles that introduce allegations, policy criticism, or investigative angles not present in the original coverage.
Frequently Asked Questions
What is narrative mutation in news?
Narrative mutation is how the same story gets reframed as it spreads across sources. The original facts may stay the same, but the framing, emphasis, and political spin can shift dramatically. We track these changes over time.
How do different news channels cover the same story differently?
We group articles about the same event and compare their political lean, sentiment, and key phrases. This reveals how sources add spin - one might frame a protest as 'citizens demanding rights' while another calls it 'mob violence'.
Can you detect fake news spread?
We track how narratives change as they spread, which can reveal when misinformation enters the cycle. If early coverage is factual but later coverage adds unverified claims, we can identify that drift in our timeline.