If you ask reporters why they write stories they will tell you that the event or story was “newsworthy.”

What they may not realize is that the timing can often be predicted because events such as earnings are on a regular calendar.

News analytics sheds new light on the phenomenon.

A chart showing the volume of daily stories over the past year for the dozen most-covered companies in the S&P 500 breaks down into two broad categories:

-Steady coverage with spikes for events such as earnings: JPMorgan, Goldman Sachs, Microsoft, Apple, Disney, Alphabet

-Unpredicted events: Tesla, Meta, J&J, Moderna, Pfizer

Pfizer, Moderna and J&J all saw spikes in news flow due to their involvement in developing vaccines for Covid. Meta faced scrutiny by regulators and critics and a dramatic name change from Facebook. Telsa is Tesla.

News analytics can also provide statistical evidence to measure the factors that are most closely associated with coverage.

By calculating the R^2, we see the size of the company in market capitalization and the trading volume do a better job of “explaining” the volumes.

By contrast, the number of employees doesn’t correlate highly with media coverage. Here are the numbers for the S&P YTD:

Market cap R^2: .44
Turnover R^2: .40
Employees R^2: .04

I find this all fascinating because when I was reporter I woke up thinking every day was a new day. That I would write about what ever happened to be the most interesting thing that day.

In fact, as big data makes clear, much of what most reporters will focus on is already determined.

It also suggests that if everyone is writing about Apple there is huge opportunity to Think Different and tell the stories of the tens of thousands of companies that get almost no coverage.