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The campaign therefore aims to challenge this widespread trolling, promote online safety and provide support to victims.
Previous research by Demos in 2014 sought to measure misogyny online.
Interestingly, this study reflects the findings of our 2014 report, in which women were as comfortable using misogynistic language as men; the 2016 findings show that 50% of the total aggressive tweets were sent by women, while 40% were sent by men, and 10% were sent by organisations or users whose genders could not be classified.
These figures suggest that misogyny is being internalised and reiterated by women themselves.
This was in response to the abuse, often misogynistic in tone, faced by high-profile women online.
However, the ubiquity of social media in society today means that this abuse, or trolling, is not exclusive to public figures.
This use of language is not, therefore, confined to one discrete online group but rather persists throughout society, making this issue more complex than it first appears.
Two types of language was classified: ‘aggressive’ and ‘self-identification’.Twitter simply facilitates research like this by providing an easy-to-access, substantial dataset in a relatively short space of time.While our previous research showed that misogynistic language was common online, it was more difficult to infer how these words were actually being used.NLP is inherently probabilistic, and with much of social media (and the words we use in general) context is everything.
Nevertheless, the algorithms trained performed well. The algorithm used to classify pornographic tweets was 91% accurate (meaning that 91% of the time, it agreed with a human analyst that a tweet was advertising pornography).
This may conflict with the UK government’s proposed Digital Economy Bill, announced in the Queen’s Speech, requiring websites with pornographic content to verify that their users are over 18.