Mapping out Violence Against Women of Influence on Twitter Using the Cyber–Lifestyle Routine Activity Theory
Why this work is in the frame
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Bibliographic record
Abstract
The study applies and expands the routine activity theory to examine the dynamics of online harassment and violence against women on Twitter in India. We collected 931,363 public tweets (original posts and replies) over a period of 1 month that mentioned at least one of 101 influential women in India. By undertaking both manual and automated text analysis of "hateful" tweets, we identified three broad types of violence experienced by women of influence on Twitter: dismissive insults, ethnoreligious slurs, and gendered sexual harassment. The analysis also revealed different types of individually motivated offenders: "news junkies," "Bollywood fanatics," and "lone-wolves", who do not characteristically engage in direct targeted attacks against a single person. Finally, we question the effectiveness of Twitter's form of "guardianship" against online violence against women, as we found that a year after our initial data collection in 2017, only 22% of hostile posts with explicit forms of harassment have been deleted. We conclude that in the social media age, online and offline public spheres overlap and intertwine, requiring improved regulatory approaches, policies, and moderation tools of "capable" guardianship that empower women to actively participate in public life.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it