@holoz0r created this tool to analyse the complexity of a hive user's content. It also shows how their writing style or length may change over time.
The readability score is calculated using the Flesch-Kincaid methodology.
This tool is created as a replacement to monthly reporting which showed similar data. It polls the HIVE API live, so no need to publish something to the chain each month, just come back to the tool and check.
Readability Over Time — click a data point to open the post
Readability Distribution
Engagement by Readability Level
Word Count Distribution
Engagement by Word Count
Content Type Distribution
Engagement by Content Type
📊 Topic Analysis
Automatically detected topics based on content analysis
Topic Distribution
Topic Engagement
Topics Over Time
📚 Vocabulary Analysis
Comprehensive analysis of writing vocabulary and linguistic patterns
Vocabulary Growth Over Time
Most Used Words
Word Length Distribution
Vocabulary by Year
✍️ Voice & Writing Quality Analysis
Analysis of active vs passive voice, filler words, and adverb usage
Active vs Passive Voice
💡 Why Active Voice Matters:
Active voice makes writing more direct and engaging. "The cat chased the mouse" (active) is clearer than "The mouse was chased by the cat" (passive). Active voice strengthens your message and keeps readers engaged.
Engagement by Voice Type
Engagement by Filler Word Density
🚫 Why Filler Words Weaken Writing:
Words like "really", "very", "just", "actually" add little meaning and dilute your message. Instead of "really important", use "crucial". Instead of "very good", use "excellent". Precise language is more impactful.
Engagement by Adverb Density
📉 Why Adverbs Weaken Writing:
Adverbs (words ending in -ly) often signal weak verbs. "Walked quickly" becomes "rushed" or "sprinted". "Said loudly" becomes "shouted". Strong verbs eliminate the need for adverbs and create vivid, powerful writing.