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

Abstract

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.

Workshop on AI for Social Good, NeurIPS 2018

References in corpus (2)

• Automated Hate Speech Detection and the Problem of Offensive Language
• Deceiving Google's Perspective API Built for Detecting Toxic Comments

Cited by in corpus (7)

• Women, politics and Twitter: Using machine learning to change the discourse
• Online Abuse toward Candidates during the UK General Election 2019: Working Paper
• Online Abuse of UK MPs from 2015 to 2019: Working Paper
• For Better or for Worse? A Framework for Critical Analysis of ICT4D for Women
• MP Twitter Abuse in the Age of COVID-19: White Paper
• MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper
• Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic