We captured Twitter abuse aimed at female election candidates and it was horrible
*offensive content warning
Our analysis of misogynistic tweets sent to prominent female politicians during last year’s General Election found that Conservative candidates were targeted for the most abuse.
The research, released prior to the anniversary of the June 8 election, also shows Prime Minister Theresa May received the highest volume of abusive tweets.
Journalism and computer science researchers at the University of Sunderland monitored Twitter for seven days around polling day, and captured tweets containing gender-specific abuse directed at 26 leading politicians.
These included election candidates who had recently served as front-bench spokespeople for their parties.
Among the tweets sent to the likes of Theresa May, Diane Abbott and Amber Rudd were comments such as “hope your husband dies of cancer”, “hope you snuff it you c*nt” and “fuck off die u b*tch”.
Several female MPs from across parliament have spoken out about the abuse and threats they face online, and culture secretary Matt Hancock has vowed to clamp down on the social media giants with new legislation.
A total of 775 tweets were captured between June 6 and 13. The Prime Minister received 700 of those — more than 90% of all the tweets recorded.
Labour’s Diane Abbott received the second most, with 47.
Tory candidates received the largest number of tweets, with 719. Labour candidates received 56, while no tweets directed at Liberal Democrats were captured in the survey.
The largest spike in abuse came around 1pm on June 9 — as Theresa May made her victory speech on the steps of 10 Downing Street, and confirmed her intention to strike a deal for government with the DUP. Theresa May received an abusive tweet every 1.2 minutes during the hour.
The next highest volume of tweets came as exit polls were released on the evening on June 8, which correctly predicted that the Conservatives would lose a number of seats, and fall short of an overall majority. Again, Theresa May was the target for the most abuse.
Timeline of tweets sent to Conservative candidates. Full graph available here
Tweets directed at Diane Abbott reached their peak on the afternoon of June 7, when it was announced that she would be temporarily standing down as shadow Home Secretary due to ill health.
Timeline of tweets sent to Labour candidates. Full graph available here
We set up a program to scrape data from Twitter. All tweets mentioning one of the candidates’ handles, and one of a list of 14 terms of abuse were captured. The keywords included “b*tch”, “wh*re”, “cow” and “sl*g”. As such, it is likely many more tweets were sent that were not captured within the parameters of the project.
We then manually sifted through the results to weed out any tweets that triggered the system, but were not specifically abusive.
The sample of Westminster candidates monitored during the project had all served in their party’s most recent cabinet or shadow cabinet before parliament was dissolved. The Labour Party had the most female frontbenchers, with 15. The Conservatives had 6, while the Liberal Democrats had 5. The three parties were chosen for the sample as they fielded the most election candidates overall.
The party affiliation of the monitored politicians. Link to chart here
Twitter, which has been criticised for its response to hate speech on the platform, recently announced changes to its algorithm aimed at tackling the trolls. Chief executive Jack Dorsey said the new system amounts to one of the “highest-impact” changes to the platform, and said: “The spirit of the thing is that we want to take the burden off the person receiving abuse or mob-like behaviour.”
Our project captured tweets and and logged them instantly. Some have since been removed, but many remain on Twitter. The full spreadsheet of captured tweets is available to view here.
The data was compiled as part of a Creative Fuse North East research project.
More on our project at Huffington Post UK.
Credit to Andrew Richardson for building the data scraper and providing visuals.
