Parler Games: A Narrative Framework Analysis of Parler Conspiracy Theories and the January 6th Insurrection
Bibliographic record
Abstract
Parler, a prominent right-leaning "echo platform," became a forum in late 2020 for Trump supporters convinced the 2020 election was fraudulent as they abandoned or were banned from Twitter. This study suggests that a narrative analytical approach to the discussions on Parler in the period from the 2020 presidential election up to the violence at the Capitol on January 6th, 2021 can provide some insight into the relationship between storytelling and real world action. We deploy a computational pipeline combining natural language processing and network analysis to uncover the underlying narrative framework, based on an actant-interaction model. We use a topic modeling approach to further reduce the complexity of the narrative framework; various community detection methods afford a macroscopic view onto the discussions, allowing one to both trace the development of narratives and subnarratives over time, and to explore at various levels of granularity the make-up of narrative subgraphs. The main narrative framework of Parler is redolent of conspiracy theory, and portrays a profound threat to America and her patriots from deep state actors, "globalists," and Democrats. This threat necessitates a response. Beyond expressing anger, people posting to the site developed strategies for fighting back. Analyzing shifts and connections in conversations reveals an emergent real-world conspiracy to take violent action on January 6th, demonstrating how online conspiracy theorizing fostered an emergent real-world conspiracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".