In the run up to both the Danish parliamentary and US mid-term elections, many first-time voters are heading online to find out more about political candidates. So far so good.
But gender bias – the prejudices and stereotypes about men and women that shape the way we regard both genders – permeates the internet and readily shapes information presented to voters. Previous research projects from researchers at the University of Copenhagen's Department of Computer Science have identified a food chain of gender bias online. That work is now further supported by their new study that investigates content on the social media website Reddit.
The new research shows significant gender bias in the way politicians are referred to and commented upon in Reddit discussions. This is evident from the study analyses, now published in Plos One.
"We see a clear trend of female politicians being more frequently referred to by their given names alone and described with language that relates to their body, clothing or family. This compared to male politicians, who are more often referred to by their surnames and described in words related to their profession," says Professor Isabelle Augenstein of the Department of Computer Science, one of the researchers behind the study.
The researchers analyzed a total of 10 million English-language comments in conversations about male and female politicians. The many comments were analyzed using statistical algorithms and several categories in which gender bias can be expressed.
About half of Reddit users, by far the largest share, are US based, followed by UK and Canadian users who represent 7.5% each. While 90% of the politicians mentioned in the study were also from the United States, the comments mentioned politicians from most countries in the world, including Denmark.
Conclusions in line with previous studies
The new study's main conclusions are consistent with Augenstein's previous research on gender bias online. Research that, according to Professor Augenstein, identifies a feedback loop of prejudice and gender stereotypes on the internet.
