Story matching · Text embeddings
How Mosaic knows these reports belong together.
Text embeddings give each report a mathematical fingerprint of meaning. Mosaic News uses those fingerprints to connect individual articles into a Story and connect that Story to the larger Big Picture.
Watch three reports become one Story.
The same three reports shown on the homepage converge into one Story and then connect to the Iran Nuclear Negotiations Big Picture.
What goes in
A meaning fingerprint
An embedding turns text into a long list of numbers. Two pieces of text with similar meaning usually land near each other, even when they do not use the same words.
Mosaic News uses OpenAI text-embedding-3-small. For articles, the input is built from the title and summary snippet, with the title repeated so the main event carries extra weight. The result is stored and reused.
Plain example
Different words, nearby meaning
“electric vehicle production cuts”
“automaker reduces EV output”
Both can land near the same topic anchor.
How the decision works
From new report to stable Story
Compare new articles to active stories
When a new article arrives, Mosaic News compares its fingerprint to the center point of existing active story clusters. If it is close enough, and the broad topics are compatible, the article can join that story.
Form new stories from leftovers
Articles that do not join an existing story are compared with each other. If several articles from at least two independent sources are close enough in meaning, Mosaic News can form a new story cluster.
Keep clusters from drifting
Founding articles count more when calculating a cluster's center point. This helps a story stay anchored to what it was originally about as new coverage arrives.
What you can see
The same fingerprints connect content to topics.
Mosaic News keeps stable canonical topic anchors with embeddings of their own. Articles, Stories, and Big Pictures can be compared with those anchors.
Confident matches power topic pages, pinned sections, and the topic preferences you can tune or block. Broad topic gates and similarity thresholds reduce false matches.
The boundary
What embeddings do not decide
- They do not decide whether an article is true.
- They do not decide political lean or publisher credibility.
- They do not write summaries, titles, or framing notes.
- They do not make one publisher count as multiple independent sources.
- If an article has no embedding, semantic clustering skips it instead of guessing.
- Broad topic gates and similarity thresholds are used to reduce false matches.