The Shape of Our Words
Conversation Fractal and the Structure of Writing
It is odd to think that our words, which we consider to be ours uniquely, as having a defined global structure that can be mapped as a predictable form.
However, research suggests that our speech has a fractal-like internal structure that can be mapped. Even more interesting, this same structure has shown up across the languages and text types studied so far.
Intriguing new research from Google DeepMind shows that language carries a memory that can be described mathematically. The value calculated is the Hurst exponent. It indicates that human language carries a measurable long-range memory (one we live with without perceiving), so what we say is shaped by what came before it. This pattern has held across the cultures, domains, and scales of expression that researchers have tested. The conversation fractal can be mapped and compared by text.
How This Tool Works
This conversation fractal tool maps text to analyze its internal structure shape. Each sentence is broken down into twelve data points. These include word count, average word length, lexical diversity, punctuation density, question density, exclamation density, first person density, second person density, negation density, number density, mid-sentence capitalization, and syllabic complexity. Those twelve points are assembled as an aggregate and analyzed using Principal Component Analysis to establish three axes where the largest number of variations occur. The sentences are plotted on a 3D axis and connect as they occur in the text.
To the side, you can see two calculations. The Zipf slope establishes whether word frequency follows a fractal shape based on word distribution frequency, and the Hurst exponent measures whether the sentences were shaped by previous sentences or if they exist independently.
What is displayed is a map of your entire text. For natural writing, the path tends to fold back on itself and take on a fractal-like shape rather than wandering at random.
This tool is designed the show the beauty of these words we write.
Directions for Use
To use the Conversation Fractal tool, enter your text in the box or upload a file, and then click analyze.
The tool will return a 3D image, with Zipf and Hurst value to the side, as well as an interpretation of the results.
Frequently Asked Questions
What is the Zipf Law applied to language?
The Zipf-Mandelbrot Law applied to language postulates that when words are sorted by magnitude of appearance, word frequency is often inversely related to word rank.
What is the Hurst variable in language analysis?
The Hurst exponent is a value related to the fractal dimension of data. Values between .5 and 1 indicate that the data follows a memory of the past (like a fractal holding to its pattern). A value of .5 is the random case, with no memory. Values below .5 mean the data tends to reverse direction rather than carry its pattern forward.
What are twelve text points that the tool analyzes to graph variance?
Word count — the length of the sentence
Average word length — the mean number of characters per word
Lexical diversity — the ratio of unique words to total words
Punctuation density — frequency of commas, semicolons, and colons
Question density — how often question marks appear
Exclamation density — how often exclamation marks appear
First-person density — frequency of I, me, my, we, our, and related words
Second-person density — frequency of you, your, yourself, and related words
Negation density — frequency of not, never, no, can’t, won’t, and related words
Number density — how often numerical figures appear
Mid-sentence capitalisation — unexpected capital letters indicating proper nouns or emphasis
Syllabic complexity — the average number of syllables per word
What do the three color modes show?
Sequence mode colors each sentence point by the order it appears in the text; early sentences are blue, moving through to gold as the text progresses. This shows you the path of the text over time.
Energy mode colors each point by lexical complexity; sentences with longer, more unusual, and more varied words glow brighter.
Personal mode colors each point by first and second person density, how much the words I, me, my, we, and you appear in each sentence.