MELODRAMATIC PLATFORMS: THE AFFECTIVE THEATRE OF POLARIZED POLITICAL STORYTELLING ON SOCIAL MEDIA
Bibliographic record
Abstract
Once a site of promise for democratizing mass communication, the internet has also become a site of problematic information and polarized affect. Contrary to claims that polarization is not necessarily encouraged by social media platforms; our two-year, mixed-methods study of affect and narratives of race and national belonging in social media discourses of the 2019 Canadian and 2020 U.S. federal elections reveals clearly polarized collective political storytelling constructing conflicting meta-narratives marked by a highly affective moralizing tone and clear binaries of us versus them and good versus evil. Surprisingly, there is very little research that has drawn on either narrative emotions analysis or melodrama to understand the kinds of polarization that take place within social media platforms. This talk shares our finding; achieved through our innovative approach to affective discourse analysis developed through iterative, grounded theoretical qualitative study; that discourse communities formed according to social as well as political identities construct these polarized meta-narratives in the genre of melodrama, readily ensuring the emotional engagement of social media users through “sensationalism and predictable plot lines of good battling evil, plots and characters that do not encourage reflection, and refusal of nuance” (Loseke, 2018, p. 517).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".