This portion adheres to ethical guidelines, protects privacy, and ensures data reproducibility while analyzing QAnon authorship with public materials.

Using rolling stylometry and machine learning to analyze QAnon texts, identifying stylistic shifts and attributing authorship through linguistic features.

This section tackles the challenges of attributing authorship to Q drops, a unique genre with distinct linguistic features that complicate traditional analysis.

This section addresses imbalanced datasets and generic differences in QAnon text analysis, creating large and controlled subcorpora for machine-learning models.

This section employs algorithms to detect and remove shared quotations in QAnon texts, mitigating biases and ensuring accurate authorship profiling.

This section highlights the challenges of building a reliable text corpus for QAnon authorship analysis due to deleted accounts and limited archival data.

This sectionhighlights Ron W. and Paul F. as key QDrop authors, explores collaborative patterns, and discusses limitations in identifying contributors to QAnon.

Using stylometric analysis, this part explores the linguistic signatures of QAnon texts, revealing potential multiple authors & new insights into their origins

This portion of the study explores QAnon's societal influence and evaluates competing theories about the identity of "Q".

Using supervised machine learning, researchers analyze linguistic patterns to identify the potential authors behind QAnon posts, revealing social media activist