Rolling Stylometry and Machine Learning Analyzes QAnon Texts Patterns | HackerNoon
Briefly

Collaborative writing can complicate authorship attribution, especially when multiple authors contribute to the same passages, resulting in a new, difficult-to-attribute style.
The principle of rolling stylometry involves decomposing text into overlapping parts to facilitate more accurate authorship attribution by analyzing smaller segments rather than whole works.
Rolling stylometry has been applied in various contexts, demonstrating its effectiveness in resolving attribution questions across multiple authors and genres.
Establishing authorship in collaborative works is challenging because the resulting style may obscure individual contributions, complicating the analytic process.
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