Machine Learning to Prevent Cyberbullying in Real Time

Monash Lens

In today’s digital age, the widespread use of social media and online communication has brought new challenges, including the rise of cyberbullying.

  • Manjeevan Seera

    Associate Professor, School of Business¸ Monash University Malaysia

With the anonymity and accessibility of the internet, individuals may engage in harassing or intimidating behaviour online, leading to devastating consequences for victims.

However, technological advancements such as machine learning offer hope in improving the efficiency of detecting and preventing cyberbullying.

Machine learning is a powerful tool within the field of artificial intelligence that allows machines to learn and enhance their performance without explicit programming.

Specifically, machine learning algorithms can be trained to detect patterns within online communication that may indicate cyberbullying behaviour.

These algorithms can identify instances of cyberbullying in real time by analysing vast amounts of data gathered from social media platforms, messaging apps, and other online platforms.

This paves the way for prompt intervention and prevention measures.

“One application of machine learning that can help identify cyberbullying is natural language processing [NLP],” says Associate Professor Manjeevan Singh, from the School of Business at Monash University Malaysia.

“NLP algorithms can analyse the language used in online communication to determine the tone and sentiment of the message, as well as identify specific terms or phrases associated with bullying behaviour.

“For example, if an individual frequently uses foul language or makes threatening statements, the algorithm may flag it as potentially abusive behaviour, and alert the appropriate authorities.”

Top view of little Asian boy child using computer tablet alone on sofa in dark room with cyberbullying emoticon

According to Dr Manjeevan, using machine learning for the identification of cyberbullying offers numerous advantages, particularly in terms of scalability.

Conventional ways of preventing cyberbullying, such as manually monitoring online platforms, can be inefficient and time-consuming, particularly for major social media sites that have millions of users.

In contrast, machine learning algorithms enable the recognition and response to cyberbullying incidents in a timely and effective manner.

However, this approach also presents certain challenges. In order to train the algorithms, significant quantities of high-quality data are required, which is one of the most challenging aspects.

A teenage student is the victim of cyber bullying, looking sad

Although cyberbullying is rife, it remains a relatively unexplored area, particularly in the context of the Malay language. There’s a dearth of publicly accessible datasets containing hate speech, which poses a challenge for researchers.

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