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 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.

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