“Pepe the frog, the greedy merchant and #stopthesteal”: A comparative study of discursive and memetic communication on Twitter and 4chan/pol during the insurrection on the US Capitol
Bibliographic record
Abstract
Following the January 6 insurrection on the US Capitol, we sought to explore how two social media platforms were being used concurrently to disseminate far-right memes and discourse. Our study employs a mixed-methods approach to collect a large data set of images from 4chan/pol/ and using the “#stopthesteal” hashtag on Twitter between 1 January 2021 and 13 January 2021. Our findings reveal how each platform influenced the usage of memes toward identity building and far-right activism in the days leading up to and immediately after the insurrection. Our findings reveal that Twitter was used to mobilize users leading up to January 6 but led to in-fighting among the pro-Trump crowd in the days after. Meanwhile 4chan/pol users took advantage of the Overton window of the Insurrection to disseminate far-right ideology and attempt to recruit and radicalize disgruntled Trump supporters after the insurrection was deemed a failure.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".