Notice bibliographique
Résumé
<p>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. </p>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».