Machine learning can detect hate speech and violence

The spread of fake news and hateful content is one of the most debated topics right now. As machine learning techniques become more and more sophisticated, numerous fields have begun to utilise these techniques. In her PhD, text and data analyst Myriam Munezero has studied machine learning models that can detect antisocial behaviours. In this blogpost she explains the possibilities of natural language processing in violence prevention.

More than a billion people use Facebook daily, and the social media platform has become one of the most influential news businesses, with an incredible ability to mobilise people. Despite community standards and encouragements to tackle hateful content more efficiently, racist and hateful material still exist on the platform.
“The words we use, as well as our writing styles, can reveal information about our preferences, thoughts, emotions, and behaviours,” Myriam Munezero says.

Natural language processing techniques have been shown to be useful in identifying harmful behaviors, such as cyberbullying, harassment, extremism, and terrorism, in text

In her research conducted at the University of Eastern Finland, she and her research team developed machine learning models that can detect antisocial behaviours, such as hate speech and indications of violence, from texts. Historically, most attempts to address antisocial behaviour have been done from educational, social and psychological points of view. This new study has, however, demonstrated the potential of using natural language processing techniques to develop state-of-the-art solutions to combat antisocial behaviour in written communication.
“Natural language processing techniques have been shown to be useful in identifying similar harmful behaviors, such as cyberbullying, harassment, extremism, and terrorism in text, all with varying levels of accuracy. However, few research address the broader antisocial behavior, which is characterized by covert and overt hostility and intentional aggression toward others,” Munezero explains.

Munezero and her fellow researchers have created solutions that can be integrated in web forums or social media websites to automatically or semi-automatically detect potential incidences of antisocial behaviour. The high accuracy of these solutions allows for fast and reliable warnings and interventions to be made before the possible acts of violence are committed. In many instances, people who have committed school shootings for instance, have indicated their intentions online prior to action. By detecting these indications, future acts of violence could be prevented.

Text and data analyst Myriam Munezero finds the results of her research encouraging.

One of the great challenges in detecting antisocial behaviour is first defining what precisely counts as antisocial behaviour and then determining how to detect such phenomena. Thus, using an exploratory and interdisciplinary approach, Munezero’s study applied natural language processing techniques to identify, extract, and utilise the linguistic features, including emotional features, pertaining to antisocial behaviour.

The study investigated emotions and their role or presence in antisocial behaviour. Literature in the fields of psychology and cognitive science shows that emotions have a direct or indirect role in instigating antisocial behaviour. Thus, for the analysis of emotions in written language, the study created a novel resource for analysing emotions. This resource further contributes to subfields of natural language processing, such as emotion and sentiment analysis.

The study also created a novel corpus of antisocial behaviour texts, allowing for a deeper insight into and understanding of how antisocial behaviour is expressed in written language.
“Finding representative corpora to study harmful behaviours is usually difficult,” Munezero says.
As the results are encouraging, Munezero finds that further progress within this topic can be made with continued research on the relationships between natural language and societal concerns.

Myriam Munezero’s PhD was approved on April 12 at the University of Eastern Finland. She also appears in an article in the newspaper Karjalainen. Munezero currently works as a researcher at the faculty of Data Science at the University of Helsinki and is a member of the Immersive Automation team.

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