How it is calculated

RSS feeds from domains, wires, newsrooms and project blogs in five verticals — crypto, AI & tech, finance, politics and sports — mostly in English. Each vertical's page says how many domains it reads today. Each feed is polled on its own schedule; a feed that has failed for more than a day is switched off.

We keep the headline, link and date. Article text is never reprinted; the link goes to the domain.

A story is a set of articles from different domains about the same news. Articles are grouped automatically by content; one domain repeating itself never makes a story.

Stories are sorted into topics; a tile's size on the home page is the number of stories in its topic.

The short “What's happening” summary on a topic page is written by a language model from the headlines, domain counts and tone of the topic's most-covered stories alone. It is checked every two hours and rewritten only when those stories have changed enough; it adds nothing the headlines do not say.

Attention is how loudly a story is being told in the news right now, as a number from 0 to 100. It measures news coverage only: not a price, not trading volume, not social media buzz. The stories in the table are ordered by it.

Three things go into it. Reach: every article about the story counts, but an article from a heavier domain counts for more than one from a lighter domain, and a fresh one for more than an old one; an article's weight halves about every 5.5 hours. Spread: how many different domains carry the story, because a story many domains picked up has travelled and one nobody repeated has not. Speed: whether coverage is picking up or fading, from the articles of the last 6 hours against the 18 hours before them.

Reach leads; spread and speed adjust it and cannot replace it, so a story mentioned twice never reads as a strong signal. In numbers: ln(1 + reach) × (1 + 0.18 × spread + 0.18 × speed). Spread is the natural log of the number of domains carrying the story; speed is the log of the ratio between the hourly article rate over the last 6 hours and over the 18 hours before (each plus one), held between −0.6 and 1.2. The result is placed on the scale so that 4.5 reads as 100, and nothing is shown below 0 or above 100. The Trend column (rising, steady, fading) shows the speed part on its own.

The scale is fixed: a constant reference level counts as 100, not the busiest story of the day. So the same activity scores the same today and tomorrow, and the scores of different days can be compared. The constant is a calibration choice rather than a measurement, so even the busiest stories usually score below 100.

Share of the day's attention, shown in the feeds and the API, is a different number: a story's reach divided by the reach of all stories in the last 24 hours. The score says how loud a story is; the share says how much of everything being read it takes, which tells stories apart when the whole news cycle is loud and everything scores high.

The score describes how much the press is writing about a story, not whether the news is good or bad and not what will happen to prices. It is not investment advice.

A language model labels each English article positive, neutral or negative, by how it frames the news for the market.

It does not guess: if an answer fails validation, the article stays unlabelled and does not count towards tone until it is rated again.

A net tone index from 0 to 100 over the last 24 hours: 50 + 50 × (positive − negative) / (positive + negative). Neutral articles are left out, so 50 means positive and negative balance, 100 that every rated article with a direction is positive and 0 that every one is negative.

Zones: 0–24 strongly negative, 25–44 negative, 45–55 neutral, 56–75 positive, 76–100 strongly positive. It is compared with the usual level, the mean of the daily index over the last 30 complete days. It describes how the press writes, not what to do.

For each domain we take its articles in the vertical over the last 30 days, only those the model filed under one of the vertical's topics, and compute the same net tone index: 50 + 50 × (positive − negative) / (positive + negative). Several feeds from one domain (rss.nytimes.com and nytimes.com, say) are added together.

A domain is ranked once it has at least 50 rated articles in the period. While collection is younger than 30 days and too few domains reach that, the floor steps down, never below 20 articles; the floor in use and the real period are always stated under the table.

The ranking describes the tone of the articles as the model reads them, not a domain's quality, reliability or bias: one that writes more about rallies and launches scores higher, one that writes more about hacks and lawsuits scores lower.

When it rates tone, the model also names the countries an article is about: the main subject of the story or the place where it happens, up to three per article, as ISO 3166-1 codes. No country is set when one is mentioned in passing, when the news is about the market as a whole, the EU or a whole region, or, for crypto, when the story is not about that country's regulator, a law, a court or a company under its rules.

For each country we compute the same net tone index over the vertical's articles the model filed under one of its topics. An article about two countries counts for both. Countries with fewer than 10 articles in the period are hatched: there a single story weighs too much. Countries are recorded from the map's launch day; older articles have not been re-rated.

Territories without an ISO 3166 code are never coloured.

The BTC correlation in Pro is the Pearson coefficient between the tone index and BTC's price change over the selected period, with the tone shifted up to a day either way. We show the shift at which they match best. With too few shared points it reads "not enough data"; when |r| is under 0.2, or no more than chance gives on a sample that size, it reads "next to no link". It describes the past, not a price forecast.