The science behind information diversity

The Filter Bubble Problem

In 2011, Eli Pariser coined the term "filter bubble" to describe how personalization algorithms create isolated information environments. Since then, researchers have been documenting just how bad it's gotten.

Modern recommendation systems optimize for engagement, not understanding. They learn what makes you click, share, and stay — then show you more of exactly that. The result is a feedback loop that narrows your worldview while making you feel more informed than ever.

The Social Media Factor

Social media has fundamentally changed how we consume information — and not always for the better. These platforms now sit at the center of our information ecosystem, amplifying both connection and division.

It's Not Just the Algorithm — It's Us

A landmark MIT study found something surprising: humans, not bots, are primarily responsible for spreading false information. When researchers removed all bot activity from their dataset, the patterns remained the same. We share misinformation because it's novel, surprising, and triggers emotional responses like disgust and outrage — exactly what makes content engaging.

This is why awareness matters. You can't outsource critical thinking to platform moderation. The only sustainable solution is helping people see their own information patterns — which is exactly what we built Mindspan to do.

How We're Applying This Research

The research is clear: filter bubbles are real, most people don't realize they're in one, and awareness is the first step to breaking out. We built Mindspan to make that awareness accessible to everyone.

The Filter Bubble Index (FBI)

Based on frameworks from media diversity research, we created the FBI Score — a single, actionable metric that captures the diversity of your information diet. It's calculated from four components identified in the literature as key indicators of information diversity:

How It Works

We use on-device machine learning to analyze your content without compromising privacy. Here's the process:

The Research That Convinced Us to Build This

These are some of the key studies that shaped our understanding of the problem — and convinced us that a privacy-first awareness tool was needed:

Why We Built This

The research paints a clear picture: most of us are stuck in information bubbles we can't see. But reading about filter bubbles doesn't break you out of one — you need to see your own patterns.

The Gap We're Filling

Researchers have spent years documenting the problem. The landmark Bakshy study found that user choice reduces exposure to diverse viewpoints by 17% — more than double the algorithm's ~7% effect. Media literacy advocates tell us to "diversify our sources." But until now, there hasn't been a simple, private way to actually see what you're consuming and what you're missing.

That's what Mindspan does. We take the insights from this research and turn them into a tool you can use every day — without sacrificing your privacy to yet another company that wants to track everything you read.

What the Research Suggests Works

• Awareness alone helps: Studies show that simply making people aware of their consumption patterns leads to more diverse information seeking. Messing & Westwood found that context cues can reduce partisan selectivity to chance levels.
• It's you, not just the algorithm: The Bakshy study proved individual behavior drives filter bubbles more than algorithms. That means you have power to change it — if you can see it.
• Privacy matters: People won't use tools that feel invasive. A privacy-first approach isn't just ethical — it's essential for adoption.