The Evolution of #MeToo: A Comparative Analysis of Vernacular Practices Over Time and Across Languages
Bibliographic record
Abstract
Drawing from a thematic analysis of 960 tweets for English, 960 tweets for German, and 753 tweets for Mandarin, this article explores how the #MeToo movement was taken up and used in different ways in the first 12 months. The article achieves this by drawing on the concept of platform vernacular, identifying three new, at times overlapping, vernacular practices: spotlighting, interconnectivity, and meta conversations. We argue that these vernacular practices function more than simply as the dominant “grammars of communication” in #MeToo but connect individual experiences of sexual violence to broader political structures such as patriarchy, homophobia, xenophobia, and racism. Yet, as our analysis uncovered, while the vernacular practices enabled #MeToo to be politicized, these systems of oppression were not always challenged, but at times, reinforced. As such, while previous research has shown how (affective) vernacular practices shape what we know and feel about sexual violence, this article highlights how vernacular practices fundamentally shape how the public contextualizes and (mis)understands sexual violence as a political issue. Overall, the article contributes to scholarship in the field of new media, feminism, and communication by showing how hashtags are taken up by the public in different ways and how shared vernacular practices emerge across languages, even when the content, focus, or rhetoric may diverge.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".