Welcome back, AI prodigies!
In today’s Sunday Special:
📜The Prelude
📐Journalism Was Always Measured
🗞️The AI-Powered Publishing Process
🦾News in the Age of AI?
🔑Key Takeaway
Read Time: 7 minutes
🎓Key Terms
Machine Learning (ML): Leverages data to recognize patterns and make predictions without being explicitly programmed to do so.
Natural Language Processing (NLP): The ability of computers to interpret, manipulate, understand, and generate human language.
🩺 PULSE CHECK
Have major news outlets become more biased over the past decade?
📜THE PRELUDE
The measles outbreak coverage is displayed across the conference room monitor, rigorously sourced and meticulously substantiated. You witnessed the reporter trace every claim, check every fact, and verify every quote. It’s the kind of story you once felt proud to publish. And yet, the conference room isn’t talking about the reporting. It’s talking about the headline.
The executive editor and managing reporter exchange a glance, weighing which headline grabs the reader’s attention and ignites the most clicks:
💚 Green: “The U.S. Government Wants Kids to Get Sick”
💛 Yellow: “5 Ways to Protect Yourself From Measles”
❤️ Red: “U.S. Health Officials Issue Measles Alert”
One story. Three drastically different headlines.
Today, major news outlets increasingly rely on AI-powered analytics to optimize headlines for engagement, whether it’s likes, shares, or comments. So, when engagement becomes the compass, what exactly are we navigating toward? If AI-powered analytics reward the first headline, what does “great journalism” really look like? Is it still about ethical, accurate, and independent reporting that serves the public interest?
📐JOURNALISM WAS ALWAYS MEASURED
⦿ 1️⃣ Measuring Success Before the Internet?
In the pre-digital era, U.S. newspapers leveraged standardized metrics to evaluate overall publication performance, not individual article appeal. For example, they frequently analyzed Print Circulation: how many physical copies were sold within a given day, week, or month. This standardized metric determined distribution reach, advertising rates, and institutional prestige. Although the U.S. newspapers with the highest physical copies sold constantly varied, the NYT and the WSJ boasted the most consistent national coverage with rigorous editorial standards and comprehensive reporting authority.
During the age of print journalism, the NTY and the WSJ had limited feedback on individual article appeal. So, some stories inevitably fell flat. Still, it mattered more that stories were “true” and “reliable” because the accepted societal role of U.S. newspapers was to serve as a first rough draft of history. Breaking major stories built credibility, earned recognition, and strengthened reputation over time. The focus was on long-term prestige over short-term popularity. In other words, trust was the currency of success.
⦿ 2️⃣ The Watergate Scandal?
On March 1st, 1972, President Richard Nixon was cruising to a likely re-election, having already withstood the controversial leak of the Pentagon Papers, which revealed a much darker picture of U.S. involvement in the Vietnam War. Yet, he couldn’t withstand what would come that summer.
On June 17th, 1972, five men were arrested for breaking into the DNC headquarters in Washington, D.C., initially dismissed as a routine burglary. When “The Post” assigned reporters Bob Woodward and Carl Bernstein to the story, they made the discovery of a lifetime. By cross-checking court records with campaign finance documents, they uncovered evidence that Nixon’s re-election campaign was illegally stealing files and bugging communications to sabotage the election efforts of the Democratic Party.
Initially, their reporting was unpopular, and they received numerous threats from the Nixon administration. Nevertheless, “The Post” editors Ben Bradlee and Katharine Graham backed the reporting, even if it meant potential financial pain and reputational damage. Their investigation ultimately triggered congressional inquiries and eventually Nixon’s resignation on August 9th, 1974.
⦿ 3️⃣ So, What’s the Difference?
The Watergate scandal took roughly two years to move from a “third-party burglary” to a “presidential resignation.” In the internet age, the U.S. news cycle moves in minutes. A massive revelation is often buried by the next outrage, making it extremely difficult for a single story to capture the sustained national attention needed to trigger congressional inquiries.
The Watergate scandal was plastered across the front page because “The Post” believed it was essential in upholding the public’s trust in the press. Today, an investigative Watergate-style story would cost hundreds of thousands of dollars in reporter salaries, legal fees, and travel overhead. U.S. news outlets look at “revenue per article” by analyzing “CPM”: the cost an advertiser pays for every 1,000 impressions of an advertisement. To simply break even on the massive investigative costs needed to pursue a Watergate-style story, it would need to generate and sustain millions of likes, shares, and comments over multiple years.
That’s nearly an impossible threshold in today’s digital economy, where a complex money-laundering scheme buried within a secret “slush fund” of untraceable campaign donations is too mentally taxing to track. A person’s average attention span has shrunk to 8.25 seconds. Gen Z strongly favors instant gratification through quick access to immediate answers that are simple and straightforward. In today’s attention economy, speed beats substance. The next viral trend would steal the spotlight.
🗞️THE AI-POWERED PUBLISHING PROCESS
⦿ 4️⃣ Predicting What Readers Will Read?
🔴 Data Collection:
U.S. newspapers track two types of metrics:
Independent Variables (IVs): The cause (i.e., “inputs”).
🟠 Machine Readable Variables:
First, textual IVs are converted into tokens: a word or part of a word. For example, the headline “Measles Outbreak” might be broken into six tokens: “Me,” “as,” “les,” “Out,” “br,” “eak.”
Second, these tokens are transformed into embeddings: a list of numbers that machines can understand. For example, the concept of “Me,” “as,” “les,” “Out,” “br,” “eak” is represented by numbers like: “{0.23, -1.51, 0.83, 0.07,....}.”
🟡 Generate Predictions:
Fifth, the predictions are translated into color-coated cues:
💚 Green: Highest Expected Engagement
💛 Yellow: Moderate Expected Engagement
❤️ Red: Lowest Expected Engagement
🦾NEWS IN THE AGE OF AI?
⦿ 5️⃣ When Clicks Drive the News?
When the AI-powered publishing process identifies a formula that “works,” it’s tempting to replicate it. The executive editor wants engagement, and the managing reporter wants revenue. These engagement thresholds and financial incentives create a “Tabloidization Effect,” where emotionally charged headlines and sensationalist coverage proliferate.
A longitudinal analysis of 23 million news media headlines published between 2000 and 2019 found a huge increase in headlines evoking negative emotion:
😡 Anger: +104%
😨 Fear: +150%
😢 Sadness: +54%
🤢 Disgust: +29%
Most news media analysts speculate that “financial incentives to maximize click-through ratios” are likely culprits. As the old saying goes, “If it bleeds, it leads.” In other words, a “good” story isn’t defined by being pivotal, truthful, or intellectual. Instead, it’s measured by likes, shares, and comments.
🔑KEY TAKEAWAY
Editorial instinct remains in the driver’s seat, but it’s being nudged by AI. While many major news outlets insist they’re “data-informed, not data-driven,” here’s the harsh reality: AI-powered analytics now wield far more influence over what executive editors actually publish.
📒FINAL NOTE
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