Researchers have come up with a statistical model to fight online abuse targeting women.

A team at QUT has developed a sophisticated and accurate algorithm to detect threats of harm or sexual violence on Twitter, cutting through the raucous rabble of millions of tweets to identify misogynistic content.

“At the moment, the onus is on the user to report abuse they receive. We hope our machine-learning solution can be adopted by social media platforms to automatically identify and report this content to protect women and other user groups online,” says QUT’s Associate Professor Richi Nayak.

“The key challenge in misogynistic tweet detection is understanding the context of a tweet. The complex and noisy nature of tweets makes it difficult.

“On top of that, teaching a machine to understand natural language is one of the more complicated ends of data science: language changes and evolves constantly, and much of meaning depends on context and tone.

“So, we developed a text mining system where the algorithm learns the language as it goes, first by developing a base-level understanding then augmenting that knowledge with both tweet-specific and abusive language.

“We implemented a deep learning algorithm called Long Short-Term Memory with Transfer Learning, which means that the machine could look back at its previous understanding of terminology and change the model as it goes, learning and developing its contextual and semantic understanding over time.”

The tools were developed based on a dataset of 1M tweets, which was refined by searching for those containing one of three abusive keywords - whore, slut, and rape.

While the system started with a base dictionary and built its vocabulary from there, context and intent had to be carefully monitored by the research team to ensure that the algorithm could differentiate between abuse, sarcasm and friendly use of aggressive terminology.

“Take the phrase ‘get back to the kitchen’ as an example—devoid of context of structural inequality, a machine’s literal interpretation could miss the misogynistic meaning,” said Professor Nayak.

“But seen with the understanding of what constitutes abusive or misogynistic language, it can be identified as a misogynistic tweet.

“Or take a tweet like ‘STFU BITCH! … I'LL KILL YOU’. Distinguishing this, without context, from a misogynistic and abusive threat is incredibly difficult for a machine to do.

“Teaching a machine to differentiate context, without the help of tone and through text alone, was key to this project’s success, and we were very happy when our algorithm identified ‘go back to the kitchen’ as misogynistic—it demonstrated that the context learning works.”

The research team’s model identifies misogynistic content with 75 per cent accuracy, outperforming other methods that investigate similar aspects of social media language.

“Other methods based on word distribution or occurrence patterns identify abusive or misogynistic terminology, but the presence of a word by itself doesn’t necessarily correlate with intent,” said Professor Nayak.

“Once we had refined the 1 million tweets to 5,000, those tweets were then categorised as misogynistic or not based on context and intent, and were input to the machine learning classifier, which used these labelled samples to begin to build its classification model.

“Sadly, there’s no shortage of misogynistic data out there to work with, but labelling the data was quite labour-intensive.”

Professor Nayak and the team hoped the research could translate into platform-level policy that would see Twitter, for example, remove any tweets identified by the algorithm as misogynistic.

“This modelling could also be expanded upon and used in other contexts in the future, such as identifying racism, homophobia, or abuse toward people with disabilities,” she said.

“Our end goal is to take the model to social media platforms and trial it in place. If we can make identifying and removing this content easier, that can help create a safer online space for all users.”

The full paper can be viewed online.