DISCOURSES OF VICTIMHOOD AND IDENTITY POLITICS ON SOCIAL MEDIA: UNDERSTANDING AFFECTIVE POLARIZATION DURING THE US ELECTION
Bibliographic record
Abstract
This cross-platform digital ethnography examines the nuances of how emotions are expressed and who they are directed towards within social media in order to better understand the phenomenon of affective polarization and the increased emotionality online. As part of a larger three-year SSHRC-funded comparative study between the US and Canadian elections, the focused dataset for this project draws on grounded theory (Charmaz, 2006) and our exploration of 1800 social media posts from the political left and right across social media platforms: Twitter, Facebook, and Gab. By examining how social media users discursively construct representations of self and other through expressions of us/them dichotomies, this project seeks to better understand polarized political identities and how social media users emphasize that their morals and values are similar or distinct from others. How do people on the left and the right feel victimized by the other? What are the moral and emotional injuries as well as the identity politics upon which they base their claims to victimhood and simultaneously place blame on the other? How do social media users rhetorically express their indignation through us/them dichotomizing, to justify their negative affect as well as enactments of revenge as moral duty? In addition to presenting key findings, this talk highlights our innovative approach to affective discourse analysis developed over the past two years of iterative, grounded theoretical qualitative study.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".