How Crypto News Is Scored: Reach, Spread Speed, and Freshness

— How Crypto News Is Scored —

Key takeaways:

1. A Crypto Attention score isn't a raw mention count. It's built from three factors: source reach, spread speed, and freshness.

2. The underlying narrative matching and classification work runs on Quuannt's Signal News engine. It uses machine learning to compare how independent outlets cover the same event, regardless of differences in wording or framing.

3. Recency is weighted using a decay function. This formula gradually reduces a story's weight as it ages. The approach is similar in spirit to ranking systems like Hacker News's front page, which prevent older items from remaining at the top indefinitely.

4. Sentiment classification (bullish, bearish, or neutral) uses natural language processing rather than a simple keyword list. This allows the model to interpret tone correctly even when the wording varies.

5. A story that spreads across many independent, credible sources tends to climb. A story repeated by one outlet, or one that ages without new pickup, tends to fade.

— What happens when a headline gets scored —

Not every headline that mentions crypto is treated as equally important, and arguably it shouldn't be. A single blog post repeating an old rumor and a story picked up independently by forty outlets within an hour aren't the same signal, even if they're nominally about the same asset. Scoring is meant to tell those two things apart.

This is what happens during headline scoring:

1. Clustering. Every new piece of coverage is compared with everything else being published at the same time. The goal is to identify instances when multiple unrelated sources report on the same event.

2. Pattern recognition. The comparison must work even when different outlets use different words and framing to describe the same story. This is where machine learning does most of the work. ML is a set of statistical techniques that allows a system to recognize patterns in data.

3. Narrative matching. Matching and classification run on Quuannt's Signal News engine. It processes coverage from thousands of sources and identifies when a story is being reported independently, not simply repeated.

— The 4 inputs behind the score —

Once a cluster of related coverage is identified, it's scored on three factors.

Source reach measures how large and credible the outlets are that cover a story.

A story that appears in a handful of low-traffic blogs tends to carry less weight than when published across major outlets. This is not a judgment on smaller publications. Reach serves as a proxy for how many people are being exposed to a narrative.

Spread speed measures how quickly independent, unrelated sources pick up a story. This is what separates a spreading narrative from a story that simply exists. A headline sitting untouched on one site isn't spreading. A headline that ten unrelated outlets independently cover within an hour is.

Freshness applies a decay function, so recent activity carries more weight than older coverage of the same story.

Hacker News's front-page algorithm divides a story's score by its age raised to a fixed power. This is why a story that stops receiving attention moves down the page, even if its raw score was once high.

A Crypto Attention score's freshness factor works on similar logic. Recent activity carries more weight than activity that is fading over time, even if the older activity was larger at its peak.

— How sentiment gets classified —

Scoring a story's reach and spread is only half the picture. Every cluster is also analyzed for tone—bullish, bearish, or neutral. This is where handling different wording becomes especially important. Simple keyword-based sentiment tools, which look for words like "crash" or "surge," tend to break down quickly in financial reporting. The same word can carry completely different meanings depending on the context.

For instance, "crash" in a headline about a hack means something very different from "crash" in a headline about a short squeeze liquidation.

Quuannt's Signal News engine uses natural language processing (computational methods for interpreting the meaning of text ). This allows it to handle contextual variation instead of relying on a fixed list of bullish or bearish keywords.

— A concrete example —

Say a mid-cap token's protocol upgrade gets covered by one small crypto blog on a Tuesday morning. On its own: low reach, no spread yet, fresh but unconfirmed. By Tuesday afternoon, three larger crypto-native outlets publish their own versions of the same story. Reach and spread both jump, and the cluster's score climbs sharply.

By Thursday, if no new outlets have picked up the story and it hasn't developed further, freshness starts pulling the score down. This happens even though the total number of articles about it remains unchanged. The score isn't measuring how much has been written. It's measuring whether that crypto news is still actively spreading.

— The takeaway —

A Crypto Attention score is an answer to one question: is this story still gaining ground, or has it already peaked? Reach tells you how extensive the coverage is. Spread speed tells you whether it's still growing. Freshness helps make sure yesterday's peak doesn't outrank today's real movement. Combined, and run through an ML engine built to recognize the same story across different wording, that's roughly what turns a flood of individual headlines into one comparable, continuously updating score.

— FAQ —

1. How does crypto sentiment analysis work? Modern crypto sentiment analysis uses natural language processing models trained to understand the tone of a headline or article in context. Quuannt's Signal News engine classifies sentiment as bullish, bearish, or neutral using contextual analysis. It then aggregates those classifications across all sources covering a given story.

2. What makes a crypto story go viral? A viral crypto story is one that multiple unrelated outlets begin covering independently within a short window. Speed also matters. A story picked up by ten sources within an hour indicates a different level of momentum than the same ten sources covering it over two weeks.

3. How can I tell if crypto news is credible? To tell if crypto news is credible, look for independent corroboration across outlets with an established editorial track record. Avoid relying on a single source being repeated across multiple sites that simply cite each other.

4. What is a Crypto Attention score? A Crypto Attention score is a single number that indicates how much real, actively growing coverage a story or asset is receiving. It combines three main factors: the reach of reporting sources, the speed at which independent sources pick up a story, and the freshness of that activity.