Understanding Newsworthiness: The Core News Values

Introduction

This lesson kicks off our first module, "Foundations of Journalistic Thinking." Our goal today is to answer a fundamental question that every journalist, data-focused or otherwise, must ask: What makes a story newsworthy?

Before you can dive into datasets to uncover hidden truths, you need to develop a "nose for news"—an instinct for what people will find important, interesting, or impactful. This lesson introduces the core news values framework, a set of principles journalists use to evaluate information and decide whether it has the potential to become a news story. Mastering this will be the critical first step in finding compelling narratives within data.

Let's begin.

What is Newsworthiness?

Imagine you're scrolling through your social media feed or a news website. What makes you stop and read one story but ignore another? The factors that grab your attention are closely related to what journalists call news values. These are the key ingredients that make an event or piece of information worth reporting.

While there are many different lists of news values, they all revolve around a common set of ideas. A story doesn't need to have all of these values, but the more it has, the more newsworthy it is likely to be.

To get a comprehensive overview, let's start with a video that breaks down seven of the most common news elements.

The Core News Values

As you saw in the video, several key factors determine if something is news. Let's consolidate them and add a few more from a classic academic framework known as Galtung and Ruge's news values theory.

To broaden your understanding, here is a second, shorter video that introduces this theory and a few additional concepts.

Drawing from these videos and other journalistic frameworks, here is a summary of the most widely recognized news values.

Here are the key values to remember:

• Impact / Consequence: How many people does the event affect? How seriously does it affect them? A story about a change in national tax policy has a high impact.
• Timeliness / Recency: Is it new? News is called "news" for a reason. Events that just happened or are about to happen are more newsworthy.
• Proximity: Is the event happening nearby? Audiences are more interested in stories that are geographically or emotionally close to them. A local election in Szeged is more newsworthy to residents of Szeged than to residents of Budapest.
• Prominence / Elite People: Does it involve well-known people, institutions, or countries? A story about a celebrity or a major corporation will attract more attention than one about an unknown individual.
• Conflict: Is there a clash of people, ideas, or forces? War, elections, crime, and sports are all newsworthy because they are built around conflict.
• Oddity / The Bizarre: Is it unusual or surprising? "Man bites dog" is the classic example. The rare or unexpected is often newsworthy.
• Human Interest / Pathos: Does it tap into emotions? Stories that make us feel sad, happy, angry, or sympathetic have human interest.
• Currency: Is the topic already in the public consciousness? If a subject is trending, stories related to it have currency. For example, during a major heatwave, a story about the historical trend of rising summer temperatures would have high currency.
• Negativity: Bad news is often considered more newsworthy than good news. Crashes, crimes, and scandals tend to attract readers.
• Exclusivity: Is your news outlet the only one with the story? An "exclusive" or a "scoop" is highly valued.

From News Values to Data Story Ideas

Now, let's connect this framework directly to your goal of being a data journalist. A dataset, no matter how large or complex, is not a story. The story is the newsworthy insight you extract from it. News values are the lens you use to find those insights.

The following article by Paul Bradshaw is an excellent guide to how data journalists generate ideas. As you read, notice how each method for finding a story is rooted in the news values we've just discussed.

As Bradshaw points out, a raw dataset often has no inherent newsworthiness. Your job as a data journalist is to apply your "nose for news" to ask the right questions of the data.

• Does this dataset show a significant change over time? (Timeliness, Impact)
• Does it reveal a surprising ranking or an outlier? (Oddity, Conflict)
• Can you use it to localize a national trend to show the impact on your community? (Proximity)
• Can you combine datasets to reveal a previously unknown relationship? (Exclusivity, Human Interest)

Your background as a software developer is a significant advantage here. While Bradshaw mentions "play-driven ideas" (Section 7) as being resource-intensive, your skills in coding and web development mean that creating interactive calculators, simulations, or exploratory maps—formats that often generate high user engagement—is well within your reach.

Conclusion

In this first lesson, we've established the foundational framework for journalistic thinking: the news values.

Key Takeaways:

• Newsworthiness is not a matter of opinion but is determined by a set of core principles including Impact, Timeliness, Proximity, Prominence, Conflict, and Oddity.
• The more news values a story possesses, the stronger it is.
• For a data journalist, these values are a critical filter for examining datasets and identifying potential stories that are relevant, interesting, and important to an audience.
• Story ideas can come from many places—data releases, current events, or your own questions—but they must be validated against news values to become successful stories.

You've now taken the first step from being a programmer who works with data to becoming a journalist who thinks about the stories within it.

Preview of the Next Lesson:

Now that you have a framework for identifying a potential story, the next step is to gather the information to build it. In the next lesson, we will cover "Distinguishing between different types of journalistic sources (primary, secondary, on-record, anonymous)." This is crucial for verifying information and adding depth and credibility to your reporting, whether you're working with data files or human sources.