Seven million tweets of violence: gendered analysis of Black women representation on social media platforms
Bibliographic record
Abstract
This paper investigates the gendered linguistic patterns of tweets discussing state-sanctioned and vigilante violence against African American women. The paper considers representations of Black women and Black death as engaged on the social media platform formerly known as Twitter. We utilize the theoretical frameworks of Patricia Hill Collins’ (2022. Black Feminist Thought, 30th Anniversary Edition: Knowledge, Consciousness, and the Politics of Empowerment. London: Taylor & Francis) conceptualization of Black feminist epistemology and controlling images and Kimberle Crenshaw’s (2018. “Demarginalizing the Intersection of Race and sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics [1989].” Feminist Legal Theory 1: 57–80) intersectionality to understand the ways that Black women are discussed, remembered and advocated for on social media platforms in relation to their Black men counterparts. The analysis of over seven million tweets utilizes both qualitative content analysis and computational tools. The paper’s findings concluded that there were differences related to passive and active language based on the gender of the victim of state-sanctioned or vigilante-race-based violence, as well as homophily, specifically name events most frequently used within the context of the state-sanctioned killing of Black women versus Black men.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".