Seeing this kind of activity on Twitter is especially interesting. The social network was dubbed as "a toxic place for women" by Amnesty International after the organization conducted a study there. Calling it Troll Patrol, the researchers used crowdsourcing, data sience & machine learning to "measure violence and abuse against women on Twitter."
"Our findings reveal the sheer scale and nature of online abuse faced by women and provides a resource to researchers and engineers interested in exploring the potential of machine learning in content moderation," Amnesty International wrote. "These findings are the result of a collaboration between Amnesty International and Element AI, a global artificial intelligence software product company. Together, we surveyed millions of tweets received by 778 journalists and politicians from the UK and US throughout 2017 representing a variety of political views, and media spanning the ideological spectrum. Using cutting-edge data science and machine learning techniques, we were able to provide a quantitative analysis of unprecedented scale of online abuse against women in the UK and USA."
Together, they found out that "7.1% of tweets sent to the women in the study were 'problematic' or 'abusive.' This amounts to 1.1 million tweets mentioning 778 women across the year, or one every 30 seconds."
Furthermore, women of color, (black, Asian, Latinx and mixed-race women) were 34% more likely to be mentioned in abusive or problematic tweets than white women. "Black women were disproportionately targeted, being 84% more likely than white women to be mentioned in abusive or problematic tweets."
Among its conclusions, the study stated that "Twitter's data states that 2.8 million unique accounts were reported for abuse of which Twitter actioned 248,000 – approximately 9%. However, the data published only reflects unique accounts that were reported for abuse and actioned. Twitter should also publish the total number of tweets reported for abuse and hateful conduct - disaggregated by category - in order to avoid potentially underplaying the true scale of abuse on the platform."
