Can graph machine learning identify hate speech in online social networks?

A use-case study for Twitter users.

Pantelis Elinas
stellargraph

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Over three decades, the Internet has grown from a small network of computers used by research scientists to communicate and exchange data to a technology that has penetrated almost every aspect of our day-to-day lives. Today, it is hard to imagine a life without online access for doing business, shopping, and socialising.

A technology that has connected humanity at a scale never before possible has also amplified some of our worst qualities. Online hate speech spreads virally across the globe with short and long term consequences for individuals and societies. These consequences are often difficult to measure and predict. Online social media websites and mobile apps have inadvertently become the platform for the spread and proliferation of hate speech.

What is online hate speech?

“Hate speech is a type of speech that takes place online (e.g., the Internet, online social media platforms) with the purpose to attack a person or a group on the basis of attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender.” [source]

A number of international institutions including the UN Human Rights Council and the Online

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Pantelis Elinas
stellargraph

I am a senior machine learning research engineer. I enjoy working on interesting problems, sharing knowledge, and developing useful software tools.