Bibliographic record
Abstract
The #MeToo movement raised awareness on social media surrounding the frequency of sexual assault and harassment and inspired calls for widespread action and societal change (Bogen et al., 2021). Survivors of sexual assault and harassment used the hashtag #MeToo to disclose their personal experiences and engage with the movement on social media (Nutbeam & Mereish, 2021). Some scholarly #MeToo movement studies analyzed engagement with the hashtag on Twitter as a ‘social reaction,' a term referring to how people respond to disclosures of sexual assault (Ullman, 2000). However, no previous studies exclusively analyzed direct replies to disclosures using the hashtag. Social reactions have tangible impacts on outcomes for survivors of sexual assault, with positive and negative reactions directly correlated with positive and negative outcomes (Ullman, 2000). While mostly positive, there was an increase in negative and antagonistic social reactions to #MeToo on Twitter over time (Bogen et al., 2019; Bogen et al., 2021; Lindgren, 2019; Schneider & Carpenter, 2020). This pilot study applied primary data qualitative content analysis to direct replies (N = 268) to tweets (N = 19) disclosing personal experiences of sexual victimization using the hashtag #MeToo, published on Twitter between late-2021 and mid-2022; these replies were considered to be social reactions in the present study. The researcher manually collected the tweets and replies using Twitter's Advanced Search function and applied purposive sampling. Informed by Bogen et al. (2021) and Schneider and Carpenter (2020), the replies were coded for themes and subthemes of Positive and Negative social reactions. The study used a mixed inductive and preconstructed codebook (Bogen et al., 2019) to gauge changes since earlier studies (Bogen et al., 2021; Schneider & Carpenter, 2020) and show potential gaps in public knowledge surrounding appropriate responses to survivors. Similar to the previous studies, the coding process and results showed that current social reactions to #MeToo disclosures were primarily Positive (77.6%) with some Negative reactions (5.9%). In addition to Positive and Negative replies, two notable coding categories that were not present in earlier studies emerged due to frequency: Personal Experience (11.6%) and Unclear (4.9%) social reactions. Future research should focus on the prevalence of Personal Experience replies to #MeToo disclosure tweets and how this social reaction impacts survivors, as well as how social reactions to online disclosures impact survivors more generally (Bogen et al., 2021; Schneider & Carpenter, 2020). The frequency of Negative replies such as those coded as Egocentric or Distracting also highlighted how social reactions can unintentionally be perceived as negative (Bogen et al., 2019); future efforts should focus on education surrounding appropriate responses toward survivors. Future studies should also inform the development of flagging mechanisms on social media platforms such as Twitter, to further protect survivors online from both intentionally and unintentionally negative responses.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 0.003 |
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".