“My first rape and death threats came in 2005,” she says. Farrell wrote a blog post criticizing the US response to Hurricane Katrina as racist and was subsequently inundated with abuse. Since then, she says, the situation has worsened: “About ten years ago, something had to be said that attracted stigma. It’s no longer the case now. Now it’s just everyday. She is very careful about the services she uses and takes great care never to share her location online.
Death threats and online abuse aren’t the only online issues that disproportionately affect women, however. There are also less tangible harms, such as algorithmic discrimination. For example, try Googling the terms “schoolboy” and “schoolgirl”. The image results for boys are mostly harmless, while the results for girls are dominated by sexualized imagery. Google ranks these results based on factors such as the web page an image appears on, its alt text or caption, and what it contains, based on image recognition algorithms. Bias seeps in in two ways: the image recognition algorithms themselves are trained on sexist images and captions from the Internet, and web pages and captions about women are distorted by the pervasive sexism that is pervasive. is built over decades online. In essence, the Internet is a machine of self-reinforcing misogyny.
For years, Facebook has trained its machine learning systems to in law and erase any pictures that smell like sex or nudity, but these algorithms have been repeatedly flagged as overzealous, censoring photos of plus size women, or women breastfeeding their babies. The fact that the company did this while simultaneously allowing hate speech to run rampant on its platform is not lost on activists. “That’s what happens when you let the Silicon Valley brothers set the rules,” says Carolina Are, algorithmic bias researcher at City, University of London.
How we got here
All of the women I spoke to for this story said they had experienced more harassment in recent years. A likely culprit is the design of social media platforms, and in particular their algorithmic underpinnings.
In the early days of the Web, tech companies made the choice that their services would be primarily supported by advertising. We just haven’t had the option to subscribe to Google, Facebook, or Twitter. Instead, the currency these businesses need are eyeballs, clicks, and comments, all of which generate data that they can package and use to market their users to real customers: advertisers.
“Platforms try to maximize engagement – enrage, in fact – through algorithms that generate more clicks,” says Farrell. Virtually all traditional technology platforms value engagement above all else. This favors inflammatory content. Charlotte Webb, who co-founded the feminist activist collective Internet in 2017, puts it bluntly: “Hate makes money. Facebook made a profit $ 29 billion in 2020.