Bibliographic record
Abstract
In 2017, the #MeToo movement went viral on social media (Bogen et al., 2021). Survivors of sexual assault and harassment shared their experiences on Twitter using the hashtag #MeToo (Nutbeam & Mereish, 2021) to raise awareness of the frequency of sexual assault and harassment and inspired calls for social change (Bogen et al., 2021). Since the movement's height, there has been considerable scholarly engagement with #MeToo (Quan-Haase et al., 2021), including research on the use of the hashtag for sexual assault disclosures and the ‘social reactions’ to those disclosures (Bogen et al., 2019; Bogen et al., 2021; Lindgren, 2019; Schneider & Carpenter, 2020). Social reactions refer to how people respond to a survivor’s disclosure of sexual victimization, including positive responses such as emotional support or offering resources and negative responses like victim-blaming (Ullman, 2000). Informed by Bogen et al. (2021) and Schneider and Carpenter’s (2020) studies on social reactions to #MeToo, this qualitative pilot study aimed to capture current themes in the social reactions to #MeToo sexual assault disclosures, as well as how those reactions may have changed over time. The pilot study applied small-scale, primary data qualitative content analysis to replies (N = 268) to #MeToo disclosure tweets (N = 19) that were published between late 2021 and mid-2022. The replies were coded for social reaction themes and subthemes based on the two aforementioned social reaction studies, which analyzed tweets using the hashtag #MeToo related to issues of sexual assault and harassment, the movement more broadly, and reactions to specific disclosures. While earlier studies found primarily positive social reactions to the #MeToo hashtag (Bogen et al., 2019; Bogen et al., 2021; Lindgren, 2019; Schneider & Carpenter, 2020), the potential for negative and largely anonymous reactions such as trolling remained a concern (Schneider & Carpenter, 2020). The tangible impacts of social reactions on survivors of sexual assault are known, with negative responses influencing worse outcomes following an assault (Ullman, 2000). Far less is known about the impacts of online disclosure and its unique risk factors for harming survivors who disclose and survivors who witness the responses (Bogen et al., 2019; Bogen et al., 2021; Schneider & Carpenter, 2020). The current study showed the importance of ongoing analysis of social reactions to disclosures of sexual victimization, specifically surrounding online social media platforms such as Twitter. This pilot study strove to centre the voices and experiences of survivors of sexual victimization (Gill, 2018) to identify current social reactions to #MeToo disclosure tweets. The current research could inform future efforts that will be needed to improve online responses and promote more positive outcomes to empower survivors of sexual assault (Sherman et al., 2019).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".