In an analysis of 2.5 million tweets, researchers found that ethnic minority MPs received 165% more online hate than white MPs
The research, conducted by the University of Surrey and King’s College London, used hate speech detection tools like Google Perspective API to analyse 2.5 million tweets by 293,000 users – including 553 MPs.
Speaking to Nosheen Iqbal for Vogue, long-standing MP Diane Abbott said: “At one point, I felt completely crushed by the volume of racist and sexist hate poured on me.”
When she ran, she became the first Black woman to be elected to parliament. Right now, there are 52 MPs from ethnic minority backgrounds in parliament – roughly 8% of 650 MPs currently serving.
“Politicians are much more exposed”
Professor Nishanth Sastry of the Surrey Centre for Cyber Security at the University of Surrey, who led the study, said: “With their increased use of social media platforms, politicians are much more exposed to hateful speech than in the past. Having a social media presence can be hugely beneficial for politicians; however, MPs will be aware that if they engage with social media, it comes with ramifications.”
Looking at the available data, the researchers found that ethnic minority MPs received 13% of hateful messages on social media. Tweets identified as hateful commonly used rhetorical hashtags such as #justsaying, #shameful and #fakenews.
New data has found that MPs from ethnic minority backgrounds receive up to 165% more online hate speech directed at them than MPs from white backgrounds.
Professor Sastry further said: “We hope that our ongoing research will help us understand what types of situations or exchanges create hate speech. It may help us draft guidelines for diminishing the occurrence of hate speech on a more widespread basis.”
So, how did the team find the online hate?
They used Google API, and then conducted a qualitative analysis on all of the tweets.
Pushkal Agarwal, co-author of the study from King’s College London, said: “Quantifying the exact nature of online hate crime is hard. It becomes even harder in the case of Twitter, where there is less content in the post. Our methodology shows that online hate can be characterised by the linguistics of posts and their associated metadata. For example, we find that hashtags such as #hypocrite, #fakenews and #shame are strong indicators of online hate. Such hashtags are trending in our data and clearly show negative emotions in users’ linguistics.
“On the other hand, hashtags related to topics like environment, education, and welfare are rarely classified as online hate.”
