Digital Affect Culture and the Logics of Melodrama: Online Polarization and the January 6 Capitol Riots through the Lens of Genre and Affective Discourse Analysis
Bibliographic record
Abstract
Drawing on our 3-year digital ethnography of cross-partisan debates in the context of the 2020 US election and January 6 Capitol insurrection, this essay examines the affective and discursive dimensions of online polarization, contributing new understandings of how genre functions as a system of norms that shapes emotional performance online. Through a cross-disciplinary theoretical framework, we demonstrate melodrama’s role as a fundamental storytelling structure responsible for the production of polarized cross-partisan debate on social media platforms. Our multi-method analysis of 5000 posts from Twitter, Facebook and Gab reveals users’ adherence to melodramatic group identities, enforced through emotional policing and mimetic identification with political influencers. Adopting roles of victim and villain, users channel emotions into archetypal and ritualized narratives of good and evil that in turn polarize political debate. Finally, this essay outlines our innovative methodology of “affective discourse analysis”, a multi-method approach to tracking and coding the social materiality of emotion through digital linguistic practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".