Vernacular practices in digital feminist activism on Twitter: deconstructing affect and emotion in the #MeToo movement
Bibliographic record
Abstract
In 2017, the #MeToo movement garnered international attention when millions of people used it to share experiences of sexual violence via social media. Through an analysis of 570 tweets randomly and purposively sampled within the first 24 hours of the movement, we were interested in answering the following questions: (1) What emotions are present in #MeToo tweets?; and (2) What are the vernacular practices in the #MeToo movement, and how do they convey affect? Through applying Robert Plutchik (2000) structural model of emotion, we were able to identify a wider range of emotions evident in feminist hashtag campaigns than has previously been identified and analyse their varied functions. Furthermore, we show how the difficulty in narrating personal experiences of violence and sharing discernible emotions via this hashtag fed into four vernacular practices, which we argue stimulate affect. Thus, the article contributes to a more nuanced understanding of two often conflated concepts—emotion and affect—and their different roles within #MeToo. The article ultimately shows how a movement such as #MeToo can be highly affective, even when participants disclose very little emotion or detail.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".