Most PR tools rank reporters by follower count or article volume. But here's the thing: a reporter with 100,000 Twitter followers who mostly republishes wire stories isn't as influential as a reporter with 10,000 followers who breaks original stories that other journalists follow.
That's why we built a ranking system based on actual influence—measuring what reporters do, not just how many people follow them. After analyzing millions of stories and tracking how news spreads, we developed eight advanced metrics that reveal who truly shapes media coverage.
The Problem with Traditional Metrics
When we started building HeyJared, we looked at how other platforms ranked reporters. Most used simple metrics:
- Follower count: Easy to game, doesn't reflect actual influence
- Article count: Volume doesn't equal impact
- Outlet prestige: Being at a top outlet doesn't mean you break stories
- Social engagement: Likes and retweets don't show if other journalists follow your work
These metrics miss what actually matters: Do other reporters follow this person's stories? When a reporter breaks a story, do other journalists pick it up? How quickly? And do they break original stories or just follow others?
How We Measure Influence: The Story Breaker Score
Our core metric is Story Breaker Influence, which measures how much other reporters follow a reporter's stories. Here's how it works:
Story Breaker Influence Formula
For each story a reporter breaks, we calculate a score based on three factors:
- Impact: How many reporters follow the story (using logarithmic scaling: log(1 + followers))
- Velocity: How quickly they follow (inverse of median delay, capped between 0.25 and 72 hours)
- Quality: Whether it's original reporting or wire service content (wire stories get a 0.3x multiplier)
Final Score = Wire Multiplier × Impact × (1 + 10 × Velocity)
This formula rewards reporters who:
- Break stories that many other reporters follow
- Get followed quickly (indicating the story is newsworthy)
- Do original reporting rather than republishing wire content
We aggregate this score across all break events in a time period (typically 30 days) to get each reporter's total Story Breaker Influence score. Then we calculate percentiles to show where each reporter ranks relative to all others.
Beyond Influence: Seven Additional Metrics
Story Breaker Influence tells you who's influential, but it doesn't tell you everything. That's why we built seven additional metrics that reveal different aspects of reporter performance:
Originality
Measures how often a reporter breaks original stories vs. following others.
Formula: Breaks / (Breaks + Times as Follower)
A reporter who breaks 80 stories and follows 20 others has an originality score of 0.8 (80%). Higher scores mean more independent reporting.
Wire Independence
Measures how often a reporter breaks stories independently vs. using wire services (AP, Reuters, etc.).
Formula: (Total Breaks - Wire-First Breaks) / Total Breaks
If a reporter breaks 100 stories and 30 are wire-first, their wire independence score is 0.7 (70%). Higher scores mean more original reporting.
Depth vs Breadth
Measures specialization: deep focus on few topics vs. covering many topics.
Formula: 1 - (Entropy / Max Entropy)
Uses Shannon entropy to measure how concentrated a reporter's coverage is. A reporter who only covers AI has high depth (low entropy). A reporter covering tech, finance, sports, and politics has low depth (high entropy).
Syndication Impact
Measures how often other outlets republish a reporter's stories.
Formula: Average Syndications per Break Story
Tracks how many times other publications pick up stories where this reporter was first. If a reporter breaks 10 stories and they get republished 30 times total, their syndication impact is 3.0.
Early Adopter
Measures how often a reporter is among the first to cover emerging topics.
Formula: (Breaks in First 3 Stories of Topic Cluster) / Total Breaks
Tracks how frequently a reporter publishes within the first 3 stories when new topic clusters emerge. A reporter who breaks 20 stories and 8 are in the first 3 of their topic clusters has an early adopter score of 0.4 (40%).
Consistency
Measures how regularly a reporter publishes stories over time.
Formula: 1 - min(Coefficient of Variation, 1.0)
Divides the time period into weekly buckets and calculates the coefficient of variation (standard deviation / mean) of breaks per week. A reporter who publishes 2 stories every week has perfect consistency (CV = 0). A reporter who publishes 10 stories one week and 0 the next has low consistency (high CV).
How We Calculate Percentiles
Raw scores don't mean much in isolation. A Story Breaker Influence score of 50 could be excellent or terrible depending on the distribution. That's why we calculate percentiles.
For each metric, we:
- Collect all scores for all reporters in our database
- Sort them from lowest to highest
- Calculate where each reporter falls in that distribution
- Express it as a percentile (0-100) and percentile rank
Example: Understanding Percentiles
If a reporter has a Story Breaker Influence score in the 95th percentile, that means:
- They perform better than 95% of all reporters
- They're in the top 5% of all reporters
- Only 5% of reporters have higher scores
This makes it easy to identify top performers regardless of the raw score values.
The Data Pipeline: From Stories to Rankings
Calculating these metrics requires processing millions of stories. Here's how our pipeline works:
Story Collection
We collect stories from thousands of outlets, tracking publication dates, reporters, topics, and syndication patterns.
Break Event Detection
We identify when a reporter is first to cover a topic by clustering similar stories and detecting who published first (within a 15-minute tie window for co-breaks).
Follower Tracking
For each break event, we track which reporters follow (publish similar stories) and how quickly they follow (median delay in hours).
Metric Calculation
We calculate each metric using the formulas above, aggregating across break events and story patterns.
Percentile Ranking
We calculate percentiles across all reporters to show relative performance, updating rankings daily as new data comes in.
Why This Matters for PR
Traditional metrics like follower count or outlet prestige don't tell you who actually breaks stories or influences coverage. Our rankings reveal:
Who Breaks Stories
Find reporters who actually break original stories, not just those who follow others.
Who Influences Coverage
Identify reporters whose stories get picked up by others, amplifying your reach.
Who Spots Trends Early
Find early adopters who cover emerging topics before they go mainstream.
Who Specializes
Identify specialists with deep expertise vs. generalists covering many topics.
The Future of Data-Driven PR
We're just getting started. As we collect more data and refine our algorithms, we're adding new metrics and improving existing ones. But the core principle remains: measure what actually matters.
Instead of guessing which reporters are influential based on follower counts or outlet names, you can now make data-driven decisions about who to pitch. And that's the real science: turning intuition into insight, and insight into better PR outcomes.
Ready to Explore Rankings?
See how reporters rank across all eight metrics and make data-driven decisions about who to pitch.
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