Women-Focused Nonprofit Organizations and Their Use of Twitter During the COVID-19 Pandemic: Characterizing a Gendered Pandemic Through Information, Community, and Action
Bibliographic record
Abstract
This study investigates how gender-focused nonprofit organizations used Twitter to advocate on behalf of women and girls during the initial stage of the COVID-19 pandemic. We collected tweets from five nonprofits including Canadian Women's Foundation, Anova, UN Women, National Organization for Women, and Planned Parenthood. Through thematic analysis, we identified nine gender-related themes: safety, physical health, mental health, labor, economic situation, intersectional concerns, leadership, the role of gender in pandemic response and recovery plans, and supporting women's organizations. A subsequent content analysis revealed that women's safety, labor, and economic situation were the most prominent themes. It was also revealed that safety and intersectional concerns were raised by all organizations. We applied the theoretical framework of microblogging functions which distinguishes between information-, community-, and action-oriented tweets. Most of the tweets in our study were informational, much fewer were associated with calls to action and community engagement. Our analysis also revealed relationships between the microblogging functions and the tweets' content themes. We found that informational tweets addressed women's safety, physical health, economic situation, and the role of gender in pandemic response and recovery plans, while community-oriented tweets addressed women's labor, leadership, and supporting women's organizations. Finally, each microblogging function elicited different levels of user engagement on Twitter, with the community-oriented function receiving the largest number of "likes" compared with the information- and action-oriented functions. Our study adds to the growing body of research on social media use by feminist groups and provides novel theoretical insights by expanding the microblogging framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".