Mapping out Violence Against Women of Influence on Twitter Using the Cyber–Lifestyle Routine Activity Theory
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".