# A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter

arXiv: https://arxiv.org/abs/1902.03093 · HTML: https://mlsanity.com/paper/1902.03093/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [Multimodal and Explainable Internet Meme Classification](https://mlsanity.com/paper/2212.05612/) (2022)
- [MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper](https://mlsanity.com/paper/2103.02917/) (2021)
- [A Study of Slang Representation Methods](https://mlsanity.com/paper/2212.05613/) (2022)
- [We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience](https://mlsanity.com/paper/2208.10766/) (2022)
- [DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures](https://mlsanity.com/paper/2307.16811/) (2023)

## Abstract (arXiv)

We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter. On a technical front, we carefully curate an unbiased yet low-variance dataset of labeled tweets, analyze it to account for the variability of abuse perception, and establish baselines, preparing it for release to community research efforts. On a social impact front, this study provides the technical backbone for a media campaign aimed at raising public and deciders' awareness and elevating the standards expected from social media companies.